natural language processing is more It is a
branch of artificial hip agents Time pills later
interaction between computer and Freeman using the national
plan which the ultimate objective off again is
to understand and make sense of the human
language I miss embody a lot about what
is a duty that is national language processing
at then we'll have a look at some
of the data on pollution Ever be on
Then we will have a look at Bab
off What a rough bias on Then we
jump in the water sequential later on where
we can insert in it Because then we
will have a new practical You need what
that is also part of people on You
could have a look a short Okay folks
uh I am money scooped up I work
It is to get a place Is that
Microsoft Also Because I can't get light headed
about So I tried a few and take
it baby students there on I also get
the visiting party I speak so recently have
started teaching Uhm I'm going to talk about
natural language processing so about that talk So
unlike the food our torture that you have
the shot So it is not going to
be as a mathematical a stark It's going
to be fun I deal with a large
variety off people So essentially forces can do
as a matter Because what about medical stuff
like that reports It's also essentially just just
ask questions as you have Just make sure
you understand and make the best record So
stop the natural language processing How many people
have heard about this story is earlier Natural
language processing NLP NMP might be more common
than natural language processing about say 14 of
a few basic Has anyone brought down any
natural language president System it text processing any
natural language Okay so it's a that snake
up off the pink because of the slaves
are also going to introduce the thing So
let's say this is going to be pretty
stating that starts with what is an LP
I talked about a few Pink Sky list
organization normalization standing in charge Then we will
talk about somethin segmentation Bruce identification when something
like this well since disintegration and finally talk
about passing so I usually do slight stuff
so I did not know so so so
basically we could also some far off times
on our stuff but more or less directly
with a slight stuff A little bit Um
let's start with what is anything So a
man wants to take that what is an
empty So what's the study problems in NLP
system I'm going to send you one language
That's the face So that's got linguistics which
is very close to NMP So trying to
understand human language is linguistics interesting So search
can because there is one NMP system which
picks which that's some sort off So basically
any search engine is an NLP system In
that sense you can call it a little
bit maybe next Mining Yes some system is
just explaining is an NLP system huh I
official So all these on these worlds right
Because I'll speak on my name to artificial
intelligence People earning these days are our information
Very well are related machine learning differences An
empty system is any system and it is
natural language processing So it's a system which
kicks natural language and process Is it as
simple as that Anything that takes natural languages
and processes it It's got a natural language
processing system Practical also by purchasing It means
of course the system should be awesome used
So if it loses another transformed The natural
language are produces insights some inside sort of
natural It's a natural language system so a
search engine is also much 11 prison system
because it takes much of language queries What
is the typical and use US insights in
terms of presents so tactical Even people talk
about natural language person systems to talk about
Raj on behalf pasts which which fall under
this in this under this umbrella So on
Start with a few examples are you know
the interesting part in the slate of course
and likely everything on the slate Then it
may be that roses are taking natural language
text of input and output Some insights on
natural language from it transform that language so
on day by only having this election mainly
because discourse to stay focused on the play
stuff So it's a national management systems are
critical competence Across various verticals are hideous segments
The Dominicans finance and Insurance a media e
commerce city did all of us anything everything
anything and everything has a bit of business
on because natural language interaction So it's basically
a big offering because all of those so
the benefits finance such so such for videos
news about companies and deport clinic stop pocket
entity much causing endless is applications that much
a language So on the application to say
e commerce tip card If it if you
want to stick it out what are people
saying about a particular all the physical What
are people saying about the sentence So what
we can do is to look at the
reviews that they have and passed them trying
to figure out sentiment people in these reviews
on Ben about what aspects people are happy
with possible suspect people need improvement are the
bad news to the improvement as as people
are suggesting thought then also expressing on natural
language persistent it said loving the fact you
know what they want So it's a mystery
to couldn't offer in across bogus applications with
an excuse like a sure popular natural language
present systems So basically you went to the
world that yes an MP is required and
if in in peace than nicely it can
do a lot of things So how many
of you I did Watson I did what
this will not know should go back and
such for simple Is that so I understand
that this is my usually rise to people
known anything If somebody saying things that you
don't understand you just go back I thought
you know maybe have such engine today So
this search for so I Watson is this
big natural and NLP system that I am
came up it I Watson became popular because
living very far one despite it again Quite
your party against the best guys in the
world So it was your party for jihad
Is this Chris kind of game You know
because you have this kind of things that
you saw on TV It some kind off
competition But they dont like me Participants on
Greece must have asked discussions and we can
answer fast is the one I think is
the one province point So I would watch
That is the systemic change Take questions off
this part of the nimble concerns An account
of the Principalities of relative Materia is fired
This artist's first famous know who this author
distribution So now of course it's a quiz
question Nobody descriptions in that sense on computers
are very good at student face memorizing pace
It's just about figuring out a nice way
off putting But there's number you know those
facts and then showing up right Right Answer
In this case that happens to be bad
That so I watched it actually won This
game is the best people in the world
and that the tempest I could realizing Oh
there is a good position for automated and
automated natural language Person system on these systems
can do something significant No I have an
example often natural language processing system information extraction
systems So information expections systems that a group
of systems by themselves hangs Mistake on structure
fixed as a skull and found make for
some structure So let me show you this
example of the information extraction system which will
make it clear what is unstructured text and
what is perfect This is not difficult human
The subject is the big into the clinic
and email beginning So I you have not
made it being It's one of the nine
tomorrow from sometime So now I mean what
can we expect after of it What they
know structured stuff we can expect out of
it meeting first time also so we can
actually imagine So I don't know what the
monkey Milk plans to use But United a
clan which picks an email off this moment
automatically suggest creation of our meeting A preview
calendar about that Actually don't at this time
of the year expect even What is the
big What is the time you're break That's
not the way you have Osteo sees the
date of the end it for tomatoes down
16% will discuss percent in that style and
drop So the same information extraction systems which
take on STUCK champions of fixed and got
put some structure stuff which is youthful So
information extraction systems are far off any of
the systems So if you think of it
still get him out when that your language
input And then they went out for some
insight off effectively insight Here is the calendar
entry It's time for a lot of folks
graphic 20 Mils and Onda I get many
off seeming so I kind of application parts
that any resistance isn't saying that this is
the same case If you like is that
I talked about it a little bit more
beauty So in this case people made a
lot of reviews so far Camera say for
example So I want to go home with
discovered I was doing in the market So
I know it's Amazons The fish You get
previous interviews I just see what people are
talking about A human I can very pretty
nicely can pick out People are talking about
It's weird they say Oh this is so
like awesome But imagine that oughta make It's
just implying to do that then briefs offenses
stating one aspect of being comfortable and importantly
aspect They look up the percent since the
start I'm trying yourself extract whether they're talking
past one negative about that and they come
up automatically that you know you are automatically
with associated with that aspect No such a
sentiment I was a system Is that NLP
system Because it basically creates a natural language
You fix on our face something off that
sort and inside a structure in sight Then
there are other systems like machine translation systems
which take a structure which natural language input
and then class for McCall of the matter
language And then they would also be off
similar operated machine translation systems which take natural
language input And then I just next toward
that can be can be used by you
my consulate So you might translate that I
could come up with the next possible What
So all of the systems are like very
awesome systems Now you are love the bigger
systems and two hours These are the things
that can be done using any pizza if
you know awesome NLP you can actually stand
Time and Vince assistance So So these systems
depend on some basic busts so visit passed
in the sense of things They have applications
in those systems No What I have been
here is honest of this nice try to
capture as much as I can about next
best NLP systems I didn't grow up in
p five it so yeah so that three
pies there's this green yellow and grey parts
So the being five is that one The
systems exist NLP systems exist such that the
family even perform like 90% accuracy and they
can actually be used nicely They commercials office
to do this There were no one wants
Bill There are some commissioner's office with that
Christie is not waiting like about 70 80%
accuracy on you can use the system Stewart
for the lottery system Yeah this thing is
going to be adjusting them But you cannot
depend completely on the assistance on the last
one that stems which led him to such
basically the same that there's nothing much yourself
It would have been even if they've got
much worse off because not basically can work
for seven restricted Gomez didn't want So you
just go And then when that one in
beef the time connection all of us have
seen how office from reduction is in our
email boxes on your local outlook Clients off
your prediction software is nobody will junkies classified
as young and pretty meals agreement So then
that is a task on this part of
speech Dug it So office Causton enter this
task in schools right So essentially you're saying
it's an office to figure out parts of
speech and it one of it Now what
is wrong what is addictive and so on
And then that was I don't think you
should always confusing you know what So our
system going back So I'm going to stem
picking the symptoms on then are waiting to
say Oh this particular buster Why do they
get basic And one is a It's an
addictive because one is amount And so this
going to buy the speech Dragon system Has
anyone used anybody Speech system I'm sure you
guys have kids and schools will probably try
one to solve one off the assignments of
Look it It's fun It's not Anyone can
just feel it that easy So that's that
then That's assistance Which big on the part
of speech talking has named entity the commission
systems But you know systems which diapers Take
it out Indignities know what I mean is
this objects Real objects Prints like persons are
innovations locations Example I stand with the U
N Officials in Princeton Science Times The person
um is an organization recognition system is a
location So So these systems out of his
next book stems are like 90% So do
not be system Not really a kid but
they're interesting things that one can do with
language For example sentiment analysis So all of
us off No this thing It is hard
sentiment Timeless is so well that everyone has
been told about it right now and uh
can be used toe a bigger now Three
things you wanna particulate review Fixed I can
automatically are used to figure out how big
practice fearing when it is large It's a
company as large You might want to see
what people are talking about the essentially on
behalf of the product they're not happy with
Yeah compound analysis So what is the future
Complained of complicated that people are happy with
And so then I saw some of the
sentiment that this book it So then that
is an interesting gospel references solution So this
is a perfect when you want to be
understand automatically a piece off fixed eating some
Hardeep artistry on many references like he she
and it And you know all this for
months Sometimes it gets confusing us to even
for your mind as to what he is
Make it right Eso Each person has made
it Imagine that you might automatically trying to
figure out this plan on refers to which
person which was nations That's what s college
for differences solutions of example in this case
that he should have done again So now
if you could actually stand for from the
bottom So you know as you once again
for immediate stands from a bottle But the
automated system forgive It is a so that's
going to split up against dissolution systems Then
the other man is Why says the sand
aggression The 1st 10% depression is important again
of anyone in this kind picks and you
want to sell You make use off this
understanding Somebody for example I need you back
these for my most Now what This most
in here So you can go back Traditionally
give you proof Senses for the white most
It's any cute Main target is up It
is an animal So which practically stands in
the world is made in a particular seconds
is what is captured Sends disintegration So that
is Westerns disintegration Use from any any uses
that one can think off the system If
you have such a system they can use
it Okay What can you do with it
Uh how bad that you can tell me
to say business income in some work are
by looking at the things that expected and
for Oh I see I see So that's
that's Ah that's a good use case Yes
I mean when I said Why says the
sand aggression it's not going to explain what
it is blinkered or is to really figure
out senses that are available Medic shitty And
then find out which part of your senses
being used here So what do you think
this is that if you can pick out
some elaboration off that particular world which is
not the past here Actually I think your
sentences on the big compartment has only connect
the second Yeah but I mean yeah that
actually they just think by this book bus
have the same spirit So I mean it's
again not going to be a so that's
not good that you're pointing out this related
things So it's different for most Director There's
not going to be similar to a spelling
correction because you know ah veteran with a
lot of different skills But here with with
the kinds of parts of the same spelling
so imagine building a newspaper Right aware somebody
says that they went to Wrangler on the
made the speech So you want to stick
it out I mean I know you want
the second of which Gundy's make it No
it could be like Gandhi It could be
he's s Sonia Gandhi and shop So of
course Gandhi about existed dictionary But again then
consider Wikipedia's addiction and then December great Or
which brand these make it Now if you
can descend the great that one maybe you
can give them a set of functionality that
while he's eating off the pitch you can
possibly say Oh if you're physically want to
know more about any almost information in this
document in the strait Chicken soup A Holiday
Gundy and like actionable insights ensure vice visual
addition did Oh Gandhi was born here and
this and that That liquored up is ah
global now and have even used So it's
a nice effort You can sit down with
that They don't want the app right on
You are discussing video thing on what's up
about recent movie you're discussing Let's go watch
this movie have the name And now what
happens is uh Long press the second button
on your mobile android phones So I said
what it does is to catch in what
is being busy But on your on your
phone wake up and what it does is
to really take that particular function thinking what
is the most interesting entity there The example
is the very Venice most interesting And make
sure you wouldn't say that the money was
the past very distinct your case Right now
you get it up from the pressure that
come and show your next next next time
slot available American indicates and saw But this
is that things could be useful That's essentially
if you think Roy says the same leadership
if you can So if I suppose they
were like always with the name and now
you don't know the choices that I want
so figuring out that I won best of
the context if you don't have the right
home then you can actually sure Simon's father
for that movie and saw that's not the
next one is passing so impossible task where
you're basically pick the same times and I'm
still stuck in the same Of course the
first is speech dragging so you basically figure
one of the five speech off each world
in the sentence But then what if a
particular expresses hate the same destroyed the window
can become by get it come up with
you know from our chemical mind to come
up with a production freeze and saw that's
going this posse the next one that it's
true We already talked about machine consolation and
permission extraction that exists off a commercial office
and use them But they're not 100% accurate
Is never really 70 80% accurate I'm that
s Oh so for example you know information
extraction systems 100% accurate They take your demons
and automatically create calendar It is for you
won't ask you Can I create a calendar
Because there's a chance to actually do some
metals getting any upload speeds That's that last
one I like Interesting So these are really
challenging Problems were solved in general like there
are some question answering blacktops for example systems
which which which are really good at a
particular moment But general So the question I'm
sitting back home Yeah What is the mission
of getting black for many Member not died
Not that huh No love for city Got
enough things No No Okay I mean not
effort City quote on a well kind of
No not checkbooks exactly the check box because
she has any platforms I'm talking about I'm
talking about on the open stock exchange and
things like that So if everything was solved
booth auspicious day like that there essentially get
office staticky and that in just such good
and says every last wishes that example beautiful
shelf that time off if it is a
particle of medicine introducing seafood and fishing the
so if it is Sergeant Google especially such
a long wait is really not Get Buddhism's
and that is what you do is to
the o R A Stack overflow and ask
questions So imagine an automated system automatically answered
in such takes In the sense of course
No Again I mean system that I have
demolished They create knowledge They connect you to
this specific example of somebody out of a
submission deficient and to make sure Tom said
that somebody is going that was asking no
system Some systems that offense in the direction
for Della very effective still still depend on
JaJuan says are good I'm so artery ask
interesting questions Andi just but some human will
come from somebody coming out so that but
then that that's okay So that last one
since a lot of people talk about checkbooks
so that these systems which are interactive in
nature the difference between question are sitting And
this check parts is that officials in systems
are interactive aspect from somebody answers at the
top that in fact indigents of Citizen Kane
pregnant and fancy school tonight so and then
discharged creep ahead and your your greatest And
then I'll toss you on the ticket on
the conversation Can Bronson says The guy can
ask for me the way but in a
conversation can brought But of course you have
I mean the contract was that we have
our wake restricted intricate public Please write your
views City I asked him for things like
video and you can ask them Prepare a
song to sing the song you know tell
a joke and so on But that's it
really cannot keep the politician group so it's
very easy for you to just pull it
on with that and then steps to get
automated computer a lot of the limitations of
the system So the movement in College is
basically copy assignments from whatever rate So one
got it solved the salt of the thing
that guys with this people's readable names and
then some of the stuff So the teachers
also not a comprehensive market blessed with off
of any immunity coffins ready booth defection gave
some protection that But now imagine a system
itself a Lipsyte I think that's explosive acquired
PVCs student and then says that there's another
sentence ABC has people picking over its weather
and says Oh by the look of them
in the same thing So basically the same
that is in a class A 50 people
If I ask you to write in their
own Independence Day I if all of your
ex similar things they didn't exactly have the
same budget that the world would be different
quiet actually out Someone's have been taken over
Our rights would be a good thing But
if it is exactly the same and being
I could pick up about with imposters detection
systems the purpose of pictures that tasks like
for sentences all going to get along with
them in the same thing the same thing
that's that last minute summer tradition so many
many separation systems In fact that used to
exist One on Roy Also for Microsoft Word
some medicine system So given a large document
to summarize it for me right So you
really specify or someone has in 10 sentences
in Propecia intensive and saw but that's the
distance are fitting limited They want to start
this expected someone division the kind of thing
that the teacher would you not So if
you remember going back to school when people
ask you a bunch of somebody that you
don't copyrights insight then since you have to
understand them going and then right in your
own words number goes so But the current
systems can only be extracted someone editions of
their Basically you get sentences ago which off
the bottom part it and then they just
without missing a little bit off more division
Sedated says the symptoms should before had anything
so on and for some linkage between sentences
that you output and saw but still extractive
somebody But what would be very nice to
do something But it's obstructive someone Addition This
is understand the meaning and then write it
in your own words in shock for example
in this case the evidence is up SNP
I wonder Jump housing crisis rules all of
the really means that they must do so
now I still don't understand what it means
behind all of these things and then computed
that when they got always doing bad is
the shark of somebody that they can write
a nice abstract So OK so this is
what it studied under the last natural language
processing So especially if somebody said that international
and the prison system practically they must be
catching one off those tasks So So the
same is not also in the past which
are natural language processing Um you know a
lot of them but one coming that I
have is natural language present us don't just
to get it fixed they will also collect
images and article speech So for example if
I go outside I take a picture and
then I tell you that give me all
that moves of the shops that I can't
see outside my next point which guidance on
those names also language prison system You're picking
natural language which exists on those images All
the shop plans way OCR optical character the
commission on then but on the inside the
names of the shops So that's that Similarly
speech condition is also our ticket system So
where you take important the father of speech
and then I'll talk the rascal I fixed
dollar bills are also interesting systems but not
limited to stick Keep extra special language with
insistence Welcome any questions like that It debates
of course that please exist in various kinds
of things But the very use the Tunis
important for example tickets can m r I
scan Are you an ice Cancel it If
they don't have fetched then it is not
a ticket with So it's basically understanding something
from the images is a separate thing that
there's basically than image processing God computer vision
people days Do they make against sex tape
against metal Good Mrs Now why is processing
We got a lot of our various applications
of natural language Insistence likes a navigator Really
great little debt Understand It's always asked us
to bathe once that system What is it
that you need to know it so this
like this captures with challenges in building such
systems So it's also before burning to this
challenge is one thing to make is matching
language is the language that he used English
Hindi When natural languages are going shop what's
this former language Which is the core body
right For example cc shop fight on dollar
also languages but the formal languages on The
biggest difference is that formal languages follow particular
court Have a sick I have a few
hours so basically you can easily find back
instead of forward language but international language By
the definition if there are rules that really
a lunatic farmer language therefore it must be
that Biggers So there must be there must
be things you know that really so on
That is the problem of dealing with metal
language that things that rules because issues and
you being stepped on So and these are
the greatest challenges that's all Big group for
example Waiting far happened See for Isaac Zida
eyes Lisbon then I plus Plus it is
the same day I mean it cannot have
different minutes has just one meaning that I
have to pull from the main uh but
languages often ambergris 11 often and with for
example the bees on reading from a mind
set off Bring people out of a meeting
So because place I pitch So this guy's
doing nothing And Spike I just likes either
kids the pictures out of strike and therefore
pizza either that nothing So let's take anyone
hospital that suit by Sanford doctors So it's
not The doctors are 7 ft It is
just that what one politic diseases So I
mean the very Redick can't defend Mini So
essentially this ambiguity metal language which causes issues
that you're trying to do these kinds of
passed on Then there is also this concept
of nonstandard English so not always been like
English Be meaningless Relating It's essentially eso tactical
Scott in English But look at the services
that you're saying are the piece that you
write in English The English Stopping for the
mixed service It may times like I think
Indiana English author planning Our country is Spanish
and English in Saw Oh and many times
they have times off shot forms like you
taught us to And so But when it
comes to match a language president us not
have been sit doing timeless is on top
of place I think it's challenging what Because
now you don't really need to figure out
what this symbol means The symbol has so
many and biggest and now but I still
miss My name's that you want us off
because of this My eyes let your language
this And then it is also same doing
here that there's a negative Yeah but then
the next one is segmentation issues So I'm
processing any natural language pieces You have to
work at the royal Move it He could
slack roads over the next Now and then
it's Carla's usually segmentation that organization say English
The usual thing is that you just look
for rights A pitcher's in public spaces You
do a specificity And given your the problem
is like apostrophes and hyphens are placing It
s so basically these guys problems for example
Look at this particle is the New York
your studio The night segmentation is here not
just one So you don't want to keep
new and rocks a picky But what happens
I mean if you just the prosthesis then
this is what happens the first one which
is another which is another challenge So it's
not just with image In fact indignation was
much easier to do certain condition Other languages
It is much more complicated No get up
So when they're not using a natural language
there are idiots in a formal language in
formal language But I am really posture itself
uses the normal basic constants So but you
know that's a long ways that are things
which are composed of Royce but they don't
mean the words that contained in the example
throwing in the towel It doesn't mean throwing
in the towel it means giving up So
if you have a provision mediums and school
again if you're self appreciate that idioms are
things beyond the used inside them So I
go in this time them and process them
especially again for sentiment that does this kind
of thing So if you're depending on worlds
but analyzing what is it with the same
very possible negative and body parts missing us
to get the secondment what way But it's
time to get the thing because they're getting
ups is basically making the same human stuff
The been manual autism's So in a four
former language when language is defined you have
set off a few years associated with it
like matching language It just also like the
new words keep coming into the language as
well as the language revolts then right knowledge
So uh so they follow language and you
like something It basically means exactly What is
it him on There's nothing like knowledge required
For example if you are if your job
was complaining it must be quick It's just
perfectly fine Ah but enough Following that I
put in a natural language They might be
same faces which are perfectly OK with the
Spectra part of speech tagging And so I
mean if you want to come find your
safety is our ticket out But it didn't
You're not You would look for English cover
I mean when these worlds we forgive you
know look forward Five speech tax And so
But they put the same places which are
probably going with For example many and Solossa
steps off each other despots if you look
it But now I am sorry but it's
a feature that is not OK on all
the prophet says But I'm not syntactical prospectively
from from English dollar perspective it's completely okay
Harry and Ron with fighting there's in all
phases that there's a combination and so on
But this is what we know Think of
that makes sense That's not tell is that
they were going in empty You have the
same topic list Um hope it must be
some knowledge base which you should Impure eso
is to understand that these facts are possible
on these these particular combinations about and the
last one is picking and 50 names So
for example if this was not faced if
I just defended us is a bunch left
what would have been I know what you
mean by bucks lately so But if I
exercise it Blake it soft means stop me
some something like that A bunch like it's
probably some including So these two key entity
names make it look like deposits Language I
like to take a look It moves So
a similar example I can give you about
the same act that I described It s
a lesson We call this up on the
phone which looks at the Okura thing on
the phone to pick out the most prominent
and getting Remember I told you about what's
up example and there's a movie name there
on it turns up at the top So
now we're doing this I have been in
the state of that And what happened Waas
Not nice talking to each other right There's
a guy named there Let's give some name
What you mean not issue just who is
talking to shut it The diamond show and
they're talking to each of it What happens
is now shallow Kiss shuttle Khan And the
fight is that the system automatically tries to
figure out and sees shouted mission many days
because you shared something shot upset something and
talk and then it just it just biggest
foreshadow saying or shot It must be the
Western part in a play date and then
lose it The shadow cut That's that's because
that I pick Yankee names at all Similarly
imagine somebody way Now There is a moment
in no way it said the ticket to
figure out the best moment in this Done
what So these people can get their names
called a lot of issues that you want
to do Automated stuff on top of these
on top of this fixed Okay so that's
it huh Any questions So far any questions
Any discussions No it's not Then get to
some little system So most of these steps
you have to report all such applications And
then of course after Babe Ruth's on top
of a specific application So that's the thing
going on 17 Start with Trump In addition
S O s infinite asked figuring out of
voice in a paragraph unifying enough Give me
all the words in the bag up It's
a very simple for you once And just
look at this twice and I mean I
automatically If I asked you to rate you
would basically make a figure out of this
place I realize it but affirmation these apostrophes
and these these I think that the big
issue example Siblings capital Now I ask you
how many ways are there You will probably
15 Lang Lang's you converted from lands capital
and sort This apostrophe is not a big
problem but the the post office problems are
It's Do it one word Similarly I am
ISAT event If not did not have a
post office in it could be careful to
figure out whether this is a stuffy you
know Like I heard I am all of
them It's find the foods No the high
phones have multiple issues like many off Three
times my friends are used to combine two
worlds which never go perfectly Must go together
You let the God now you cannot get
rid of the iPhone and then destroyed you
Experience the card says you They're not worth
just one single word but many fans It's
OK to remember about They don't die even
if you're still that has four different kinds
of consider 45 watts It's completely okay On
many times these high first has to happen
toe exist because of for mating issues like
kind of a line or a kind of
a page The five months exists and you
must promote themselves to But then what That's
now the law cases when the spaces at
all times since North cases in general a
single word But many times they have some
fun words But you want to speak at
us North biscuits so that like there's not
just one what actually was it on Oftentimes
it is impacting producer So for example you
know you are amazing Reached about the mortar
position you want to take it all Public
safety might be modified version but I wouldn't
do that Hey if you want to take
a secondment about you wanted that they should
inhibit about Yes that is a well you
can dig 100 forms Great on go to
the police time There you ask them You
know I one time where I don't want
to get on the past right here Because
there's a present a specific type of population
that summit must find themselves So it must
be that one Good on this is like
basing hospital in officers inventive lately That's that
one will bring Good What about it From
picture Yeah they come back to that huh
And it just a little bit bigger It
was a tight so basically sentiment discarded and
ridiculous Must be the fate but they get
some social media So you can basically look
at all these things from happening But it's
not difficult to do that because uses delegation
on two separate fires Are they have GPS
able move all weekend All the piece Which
was it from heaven above have been my
position in it Oh no Someone that needs
can contain hash movie So now you know
And of course that's uh that I do
have a mission Automatic basically says Hash Big
movie It's a single world big movie now
Hate movies like the one in Your Picture
you don't know if it's positive and negative
You can say Oh man is going does
not like the morning It's a negative Thank
you mean basically it is for these visas
that you need to really split this compound
words many types Your hashtags are other ways
Such compound words you can't just tell them
in a single word on Just be okay
Being various it'll be tough You have to
split them so as to be able to
build those basic worlds And then that sends
make any sense So that's that It's a
little bit of what's next in front of
school It will the space that you don't
want a space along the store sense of
directly in front of specifically they don't mean
anything if you don't eat of it after
we have a single in the opening that
and lastly that these that these equitable So
my spot I want PhD that you might
want to store only Yeah only only a
current part of applications You might also want
the front office especially especially for again for
safety Mind dialysis You have a lot of
slang which is you want to expand And
then I didn't get the same treatment Getting
words out Okay in English things out isn't
really easy Spaces usually work nicely on We
know that we have today with hyphens and
apostrophes somewhat But in other languages life becomes
more difficult For example in German now compounds
are often begins together Anyone was dead one
here Denver Padilla Then I think it was
so basically I know some sitcom pick dumb
unknown compounds of much significance of example if
one has to wait Life insurance company employ
The Reagan didn't enjoy miss this big street
without spaces But if you have to support
any kind of application and the application you
must get these four things separately over the
world A lot of it long So in
this instance language insist on splitting the bill
Must you really need to figure out how
to really divided despite locking interfere different chunks
so so far it is like is good
but it becomes difficult moment of the languages
so that Chinese and Japanese Chinese then must
visit it so people don't expenses So basically
Chinese paragraph would look something like that Then
this person it on unless a cynical and
something of that sort After Mitt you'll find
a scare someone off the steps and so
on Obama you know first us I mean
the first thing that has to do with
the smallest in they don't distributed it appears
to take rights right How big since the
destitution is a problem Uh yeah on the
interesting part is Japanese even their complicated that
must visit it all on their ultimate like
convenience of Japanese in the same sentence So
imagine dating from the uh the sentence which
comes just offer some part in both pretty
some fight And I just you know model
idea and saw It's a physical A combination
office that dialects obvious meetings often language in
the same sentence on the That makes it
even more difficult Food Big words out of
such things You know I have this problem
Essentially you have the kind of statement of
it actually should be drinking like that You
have anything that's this is like tax That
I mean they wanna listen You start system
How is a sentence at the top This
is the thing that you have So what
do you mean If you want If you
want you on the spot About 50 Voice
Any idea you are actually sit For example
which lists all the worlds in the in
the language the example You have this state
and you say you have this kind of
thing And new Brother city huh What did
this So what will be the same after
this one He would be there in the
picture Better but yeah Ah but the A
g Ok And then what Uh but then
there is no such an ascension And still
you have a summation Preached basically And you
want You have hashtags and you want to
break them So So there's no such about
something evident in this tweet the money Is
this being me Hash Yeah Okay Okay Thank
you Yeah Yeah that's right So basically what
you do you basically and get out in
general So what You take it Who wants
to start from the beginning And generally oftentimes
what makes sense is Max much segmentation in
the sense that you start with that dancing
first it doesn't make it existed It actually
So you don't want the last key And
then you're such what I don't think it
makes sense And then you lifted whatever your
lift it right and the new such you
so that I just just what That starts
with you I'm just in the dictionary and
then save your PhD Then you find out
of local see left is what you do
the same thing Start doing that again with
the many parts It's going back to my
segmentation It's basically the state of the my
match emission It is going to do this
thing for example the think I mean what
would you want to read out I think
people don't bit so that's that's one possibility
But if you do then that's much segmentation
right I mean of course if you know
what they say I mean you start with
the smallest one You probably get that But
this modest man also half its own issues
So I'm trying an issue with the largest
one Then what would happen if you start
with the maximum Yeah huh That that is
always I don't know Yeah I know I
don't do the work happens it So now
I know you get something but you know
it doesn't make much sense It's an allocation
Is this not what they do You have
to do things right I never child which
hasn't tactically correct splits off the but they
only choose one Yeah a little business if
I fixed it For example if a lot
of police were also fighting movie someone hood
and that you were basically say OK hash
what did Basically spitting indicating what did that
sounds because it was talking about I mean
the street has morning somewhere the user has
used moving some people speaking The other thing
the same thing is something n gram models
So let's not go back to this car
but hold is used So what you do
is to really pick like a offline set
apart in no someone in the back you
did last year last one year finds it
in their pockets in the documents they last
one years Bank of India I pickles and
then you figure out how many times our
affairs office as is the example I don't
know anyone OK maybe but anything So that's
saying something Combinations that much more secret Uh
stuff combinations I'm not secret For example that
down done there I think Much more secret
than Peter Blood blood or one bit So
I'm the bad pills here that the probability
are the possibility of seeing this unusual This
is English is much more than what you're
seeing this stuff So based on this problem
computations you can basically choose the best one
off various possibilities Mixes Yeah I know you
can So I make sure one year off
kind of guy because people essentially are because
I mean just down and number 50 inches
in your fifties I know Yeah I think
basically you can't so much I think this
game Is that a problem So there's nothing
much they can do You have some context
If you have some context in this business
I have to go Tonto So if you
have a contextually convict the values in the
you can choose the question and I never
chose one Are you confused It's not usually
because of colleges and it'll be Yeah I
mean it doesn't get any and saves It
basically goes down with the biggest So that
just like a software then going inside you
could Okay so how do you go with
few options that's have a suspicion Basically it
with Michael options What you do is to
such what all possible words that start with
Pete exit it They could be dying Could
be And then I will say you have
to work to start your feet They Peter
So then you expand on that a few
possibles thinks that I've been using men off
the match that I was talking about You
use on and then talk about common Okay
so this is a nice need English bird
of Corsica As a mission you can use
probabilities no from a large collection of documents
to be able to figure out which one
historic don't work in English with plates but
interestingly the people Chinese their wives such that
they're not the fixes the time life perfectly
language basically a perfect What does that mean
That basically means that 11 has gone up
the fix of other work for you understands
Perfect It's perfect So if one for example
diet picks off beat up In a sense
it starts the another It's basically quite a
perfect so in China's It turns out that
there are not many for fixes which basically
makes that make sure that the couple go
there were simple under the most that What
is it for organizing Chinese seconds Okay the
next step so essentially event or condition may
have brought in the woods but many times
what's assistant Various forms For example you will
say the same S U S stock A
doctor is the same as America is the
same as United States of America Know all
of this mean the same thing Essentially the
nomination is a prisoner normalizing so basically far
information to people or even for certain since
late If you're searching for USA you also
want a page which basically has you got
it You must get off Bring this kind
of face Essentially somebody searching for us traduced
e like much You're uh then he implicitly
defined So basically consol that double what became
There's just leak the video of the sea
What Sometimes what we also need to do
is to do asymmetric expansion Okay Before bringing
the day What happens if somebody searching for
you that is not enough Still there should
be showing up It's us It is If
somebody says USA it means you know somebody
says You're not but not for anything Thanks
The civilization ship is that I think What
does that mean So let's take an example
if somebody searching from window just just window
it's OK to show Bring Alvarez windows from
my setting for Windows Then it's OK to
show off Microsoft windows on uh you know
somebody for fixed window that your windows for
the halls and stuff But if somebody searching
for capital windows it's really really by the
shore of the pace with which you know
the windows Because if someone has a capital
looking mean Microsoft Windows for their 1st 2nd
expansions Second automakers are expressions hold one way
but not that we don't So that would
make a good nature The big number edition
You must tell us to make sure that
you bring it in a nice week so
that's not the next five guess fielding so
many times So they think this text processing
right people that look it's everything because this
is cause an issue So many time's uh
you don't want to really make a defense
based on the case So which holds in
making sure that you can you can self
things that need the same thing Let's take
used to it if you're searching for something
to for example for example searching for the
National Institute of Technology Heather about the fungal
relates that we think people will replace him
about But you know the type international stood
off the largest and not going to type
I capito I kept the talent and capital
and shocked the most of the time Stories
are all in small letters but for many
times what people do as a long time
that there were some text comes to your
usual lord Yes everything But that pales in
such cases So in some cases if you
are so shaped so any like capital letters
here pretties Have you your police They did
a capital letters in your party So getting
Give me an example Like everything with us
Yeah they were searching for some End up
breaking your co Are some some better message
You just get a copy this stuff and
then you have capital letters every time he
is heavy reduced kept emitters magnetite equities Have
you ever used a bit A little bit
No Have you such car for a condoms
for example I mean essentially if you are
searching for general motives just try searching for
without any capital letters Now General Motors can
mean 18 General Motors with a capital G
capital mbanza company The middle if you are
searching part staying still fired off in limited
cases When you are when you are dealing
with them to certain cases that you should
not notice all the characters in the world
on a per case it could came on
according on that it in no case it
would be in completely different It's still about
half a kilometer wasa sailing a ship so
mean completely for intensive Just look so well
you guessed holding on deficit place It's involving
for seven applications but you should be cautious
Might bring that Is that what So in
fact a second cases like sentiment analysts use
machine translation of cases actually being perfect So
for example us watches us if you just
Milken's things us I'm in the US very
large men u s a to become US
recipe So the next investing So these are
all things that you saw me You know
if you bring any natural anger prison system
because help simply fire fixed simplify help You
understand our extract meaning out of politics So
this one is a limited edition OK so
in language So it so any investing paints
so consider any particular word So then various
citizens why you transform this world So for
example you looking running you make a Why
did you do that But I didn't bounce
five words under this since basically they reflect
the tense is why you ask anybody for
them to make it a deer and got
a world like mortified paints to make it
you modify thinks cat catch Dr Balls What
What do you mind if I waas We
better best week speaker Right on And so
also basically for dignity are intensity You modify
words What iss far Making the economy right
so important Unimportant You're wanted by the world
for the very reasons So for example quality
anymore If I was a part of keeping
our eyes off my vision offered I used
often izing them on farms of the same
word No money had been such They said
If you search for something like that my
wife is what you know If it was
some document which says our tradition from we
want to match up knock Of course Of
course you want to match in the sense
that if you're searching for some some automation
thing you do want to match that authorizing
middle tradition therefore rate And that makes sense
to limit eyes things I don't bring them
to the bus stop The limits division is
the person removing these elections are very crops
The fact of the baseball I think so
because like a piece off course between pick
do this wise and for example the voice
carpet effectiveness will become something off that night
Interestingly a nice the Canadian tradition It on
this form find that dictionary words in the
sense that it was just really worried because
God and what it does finally wants you
to present an addiction Nice part of our
limited it eso remedies It is good on
such so for example for such missed applications
it is very important travel Everything is it
thinks that should match match because of how
I imagined this fall But in some cases
it is back to use limited but example
But this is that these things changes all
the way over these convictions as that So
for example living machine translation in Spanish the
boyfriend means I want let us if you
want But in Swan if you memorize them
then I'll become Crip and then lose the
inflection A limiting Basically this particular tradition happens
because you want to say I want If
you do limited edition when your machine class
mission that's bad But if you have a
limited edition by you're going see matching our
Web search are any such in your large
collection of documents It's good vixens for this
Got limited edition Yes the next 200 stemming
Masten is very similar dilemma condition but still
it is actually Ah NIU sense It basically
just takes a while and then chops on
the last few things So for example if
I asked you to limit those things but
what do you do Basically running and then
my auntie for most of the time and
you get that it's over what it's all
about You uh human shape et et from
that And you get the this look for
example automates automatic automation are reduced to automatic
So it's basically just during the last few
that this thing would become that if you're
if you're sort of Afghanistan So the nice
part is that you do want to boost
to me upon large world so that things
can match so that it can actually match
to have anything document But then on then
you know the problem here But stemming is
that you may not always end up with
the world would take this in the dictionary
But that is and people have a problem
because you cannot really then give up the
stem are awkward for users If you want
the beta system which your stomach you want
things But it was nothing that basically saying
it's good that the output is human rights
exist in the dictionary Okay so how did
this time in a different I mean any
kind of interesting rooms that you can talk
about this Tell me so I only thought
about beauty You need todo huh Right That's
basically could be moved Yeah and then make
ricotta week and saw visit by a linguist
What He wants to come up with a
set off You know these things and then
they can respond Sports already popular standing on
them in fact the most populous them apart
English has to come up if you don't
solve this fall that enterprises wounds one by
one so like goddesses will become caress Guy
should become so and then you know he's
in a state that let's not no idea
what broke us because then things will become
cape and the fact what you want Oh
the beautiful working class to really I am
Do you forgive in a moment in the
remaining part of the word on that table
observation So I don't know a distinct that
Usually I am just a great forwards which
have about within them So and then there
are other times off that he proposed It's
basically like a relational to relate on a
revival to revive and saw Today I think
usually used understanding and then whatever is left
Basically God bless them off work No I
mean you would ask that by Vitaly propose
to figure out I am the only vendor
event over Let's on any did Western doing
experience This is what's expected Walking should become
rock but saying should not become s to
become the main thing So you get an
experiment So they put Shakespeare's clues eight to
the brig Our architecture our experts please and
then figured out of all was that ended
I n g And then he started them
in descending artists Own evidence If it out
that thing wasn't any of that you put
them on many fair second Not that that
this is that if you look at why
stop in words which came in i n
g He saw that I d on you
make some sense If I don't know I
can help me get to the fox being
be on coming CEO Seo E But it's
OK Everyone say CMAs will step up So
they then he detailed on sort of combinations
And then he said that let me really
look at only bills which basically and then
I empty as we have a voluntary mini
part and then saw them in defending order
And then it's United won seven out often
actually makes sense when they actually have no
i n G And that is why Because
how he came up with this route And
that's how I mean his design All of
those rules which basically okay no myself But
they have a little issues says that standing
is not a context sensitive For example if
I want imagination idea that I was searching
for Microsoft stop place on that You know
the Web setting in is using stemming don't
stand stocks Microsoft Stocks Place are didn't stop
Stop And that's my members Ah pitch on
your plate with the selling stockings now interestingly
what this guy's going to do what the
stammer is todo stockings to stop and then
it much basically talks about Stop so and
that is a really bad much It might
be some stock but it's not the stuff
that I wanted in 60 The issue that
can't happen if you're doing stemming contest insensitive
it So it's risking not a perfect ippon
text and then at the party What does
that letter Yeah what it does is to
look at the context So let me limit
price to get the car exploits to understand
tradition with this So for example that a
about uh somebody is using the world saw
it said The word is used in the
world for all the way from the virtual
limit has to see But if it is
using the nonfarm limit I would really really
people saw it not limit eyes and shot
but a stellar We're really just use the
way of the double off the end and
then just always So that's all I know
is useful for long words but because it
is my sporting anything But then it is
also what complicated right Because I am a
Kaiser has to really depend on context and
this is it has to make up the
contest Pastor Figure out what is the what
is the sense are out of the part
of speech in which the particular world is
used and saw Okay so that's a lot
for decision Any questions So far any questions
Has anyone done anything off that sort of
limit Izing doctor never said anything like that
This is a city and let's hope it
is looking though and then you can type
in whatever you like So again this is
just some sentences So second type in I
am teaching a lot in But you Bernie
What else So at a time like now
it is 3:30 p.m. On 19 November 2000
and 16 They know what is Uh um
I could be in so you can do
some more pain More You just don't think
Okay let me just put it out Uh
yeah So what Both put it in Uh
be we are in our hotel Yeah in
about in the office It is not us
it But I just want you uh one
of the things And that is why I
basically do that So uh let's see what
happens What This guy does it really make
that stuff and a lot off natural language
processing As he talked about a lot of
things Get off the boat Hoping you talk
about Oh hopefully will know Something's Yeah State
bathing gloating gloating lording Uh oh I see
There's a lot of things going on on
this machine Okay Yeah Yeah So so far
folks who are doing it on get on
machines they can actually see somebody 79 if
it comes up in one minute It's good
Otherwise I also have a slate so that
I have like uh proper first aid stuff
because at various places Internet connection is a
problem Okay so it's a big come up
I hope it become about really discuss this
Like it So I think something is uh
only point This is a family grew in
love this thing more We've been led the
stuff So this was not just think this
there was no Didn't think there is a
good thing that we get to it Okay
I mean let's not go do it and
that's in large Okay this is what this
is what it does So basically walk it
is going to do is to do any
figured out that there are three sentences in
it first gets the best sentence segmentation And
then besides in segments that initiative also dustup
In addition because going to figure out what
was in it I did not have any
I am I would kind of like people
conduct ical also organize and figured out that
down my words up that and then for
each other's rights it also gives you up
like a speech for example Here it is
Mark My name has now on BP's unknown
a out and then is a world beating
is another world VG is we're dealing and
so so essentially act as if the position
I m is the morning after Prohibition I
lost isn't known and shop So this is
the first thing it does so far Speech
tagging Let's talk Oh it's just it has
get up in fact Okay Yeah Hand Menino
stuff stuff stuff Let's see Yeah give it
sometime if it comes up great inside of
slaves And you So that's good The besides
the party speech talking One other thing that
it does Yeah I know guys who have
laptops are having fun but for others it's
a little bit of a problem This people
I'm struggling with this Okay You okay So
this is final speech document Also those named
Amputated Commission So what is meant by that's
essentially that my neighbors think about that person
and they think about losses invocation on But
then also figured out that heaven about location
It also figured out 6:30 p.m. There is
a time on Yeah I do So that
was a good example That's why we have
19 year But that's a big distance So
imagine if you if you are the one
say you're working in a company and somebody
asked you to you know take that title
will be um they Vicky or the collection
of the company And somebody tells you get
me all the names of the projects Nation
in these documents So you want todo all
projects that have running the company so far
It's a very nice applications especially can transit
checked in particular entities and then liver them
like that So that's one So then that
I think that this demo dies mustard really
come up But this death all this correspondence
resolution So remember animation minute provided an example
off poor defences Solution What was example I
told robotic that issue run again So what
does he defied to The same thing is
here eventually made it difficult for that So
you get so basically that's not the best
evidence of a solution And then the last
thing it also does is to figure out
these basic dependencies So which is a lot
of interesting stuff So so here these dependencies
basically hated sort of problems like subject verb
object identification You know better schools that also
everything that he used to do So nice
to pick it out That beating is the
world And then on subject If I never
see thes dependencies that basically paralyze dependencies between
words but this and subject tendencies This going
to that this particular world real It's this
now So basically replace the now Yeah on
then I found the political too that this
world is not since it basically is that
is And then there's a defendant physically here
that this is the object and talk Essentially
all these bills dependent piece often hear your
biggest aspect serious synthetic aspects that one can
discard from a sentence My new segments Okay
so that was that them apart Now let's
see OK this technology has come up In
fact is there anything interesting again showed that
they think part that I can show here
is this follows So which was not done
on the slaves There is a distinct parkas
You've got stuff So it did not really
so nice Part of its difficult about the
piece inconsistent with the same thing Speaking was
done just burst on the dock is located
Amazing way part of a single sentence That's
that And uh then whatever So basically Oh
any other fields for some reason Yeah usually
does not Mostly it is because off basin
it connect with your what are those also
feel pretty Yeah I mean there is nothing
else Nice to show here Just because things
feel reasonably it's if you if you go
back home you can destroy it Stanford Cornell
P demo on You'll get that And then
you know the description off these That these
tags Now what are the stats on the
stand for You can really such for 10
3 day our speech bags and you will
get the bidding for Basically Prp is a
prolonged VP's Some work so well past participle
or something of that sort I don't know
exactly what but disappoint a good kind of
woman saw So such for every bank penance
be And then pre Danker uh yeah can
start Sure but I'm not sure if it
will work Yeah I don't even know me
Bank Yeah Uh this is good but basically
up Oh my The problem is about Well
sentiment done This is part of sentiment nine
Mrs There's no straight package I mean um
then that means so there is a package
opium fixed mining package which allows you to
do all of those things that we stop
But if you talk to a limited edition
said stemming hiss fighting a lot of these
can be done using the using text finding
package sentiment analysis you'll have bigger on Usually
there are some software like this but there's
nothing like a package in our okay No
stock Yeah that's a good question again Now
you're asking interesting questions So basically that's godless
named entity The cognition Remember one of the
past that he talked about a building the
week of the boat I stand make the
U N Officials in something like so list
Best court used to look up approaches one
pinkberry How Dictionary off all names And then
maybe the man matches in the victory in
That is why I say that as a
person But they have riches beyond that For
example if something is teaching them that something
must be prism development in the context One
second ago Yes And we make has indicated
my name Cruz Yeah Did you protect with
capital The first word OK Yes Sometimes it
works not basic rights but sometimes it does
what it's Ah Maybe the name that you
put in is not very common So by
putting in some form of me in the
sense uh something that can exist in addiction
So it's not just beyond the chain Look
up for many many times It just takes
one personal context tries to guess what is
the thing So what is it Just that
thing as whose data indicate that this market
nobody's so Mr Advantage wants something different No
And it has come for segments Imitation sentence
and condition in general is very simply passed
Basically it's a task is you're going about
it if you want to take it out
Sentences already do that If you want sentence
If it what would you do Full stop
saying in the forefront stops You also put
exclamation marks and question marks and exclamation marks
and question marks are reasonably easy right Remember
respect Stop that All this stuff the media
despite Ambergris sometimes with triple site that's found
in most financial sectors quality But sometimes it
would be a part of an abbreviation Late
doctor i N C and song And sometimes
it could also be a number It could
be a part of the economic You don't
start so I almost want to really consider
stuff are back as a stuff What you
want to do is to be the by
the declassified I have you know the classified
Yes it's okay Yes Then in that case
what you do is tow a classified is
basically a machine living bothered So basically say
you tryto make some sort of rooms in
the model in the sense that what could
be booze to figure out if a particular
dark is up Yeah I mean the next
world must be capita Must be must be
starting capital later This particular right should not
be starting with a capital letter There should
not be uh space Yes have that exactly
If there is a number that will not
get space in general after better food stop
If there are new lines after the food
stopped the next like in the fire which
busy elements It's also end of sentence and
stop the various rooms American Incredible enough Finally
classified But you know model this on these
100 Andrew there are the fruit of the
world after the doctor left off the world
with the dark So if the eyes like
the are you sure the conditions and so
taken all include all of those homes and
then build a nice Sington signature Okay so
the next task of and I don't know
that time So this is the time ended
at some point So 48 assistant sometimes Um
the next task is like his identification So
whatever friends and gender anyone gets ill defined
order please Combination of course Is every combination
huh Which makes somebody exactly it's abomination off
rice Which makes meaning beyond that critical This
particular words madam since example blackest is a
face because let me just have meaning But
when combined it is a specific thing A
concept is what you can call a freezing
in some sense So I wouldn't find physicists
physically if I give you like like I
asked you give me offices in the static
enough That any ideas How would you find
food is in a panic Enough huh You
must have this stuff Is is that's off
course If you have a list of presence
you can exact matches and then figure them
out So that's that's one way of doing
it right I mean that's very difficult to
obey on this stuff Yeah So you can
basically start with the one World and then
say Oh doesn't next work building this one
I'm not For example one of examples about
like this Is that a first again Last
one year times within their compass Our national
documents They never blood observation How many times
it was covered by best if a lot
of tough times black test or blood interest
Okay together what sister Over individually that means
that Mr Booth I'm in that case you
would put blood in just to give it
and then you go for a hard work
Maybe the time is not hurt too many
times with the test And then before you
know and then say Oh my face it's
it That's one thing And that's basically the
part of it You know one of the
vaccination here but it is mostly about using
something that's mutual information the mature information between
two worlds the waas a lot Together there
are mutually quickening words and that which is
done is done suffering approach in sentence crossing
a fruit which is very simple physics What
do you do Because it at all times
for its between stuff English as someone had
stuck with Stop this Dr No The stoppers
on ones which are tough Any works like
a dog on And um stop Woods So
this district a pretty decent pension and all
the boys between stop It's in the sense
this night for definition you're your expenses and
the limitations Yeah our present You would use
the stop us as looters So for example
the previous late if I went to London
uh praising approach are perfect This is what
happened So is to as next more down
stoppers Because there are too many times in
in English on anything that is separated by
Bill is like a foods For example indexing
units replaced Black River Black Bear's bed home
just wise index elements that all books freezes
because you need to fish versus the spark
after baby space If you can't remember which
I just let one like just talk among
in the past But you can remove that
saying you should have a key Stewart's So
it is very simple really easy approach for
fizzy that always requires that actually off the
stop words and they should have stopped It
is very easy to get I mean pick
again Last one year on offense of in
the office And then it was a hope
they took a lot of time So like
I will contain a and many times on
the Yeah Now your first off it's on
from large offices They are lush states Then
this is what you get Were all interesting
credits if you look at and in fact
they contain a lot of locations check your
education So remember that New York example which
was screwed up because the world you are
using space in a clinical Now if you
have done nice prison you would know that
New York is a single thing It goes
together on before you're not You're not split
It wasn glass space But people every single
indexing unit Yeah uh most of them It
sense that could be a few things which
which does example Example One table one They
come up just because they taken from us
Pickens cough us But most of them actually
make somebody's rebels it's now that you do
Freezing is to really use a think That's
kind off prosecutors He passed three Remember on
the very first life that I was talking
about wages and because I told you that
you can also build are constantly posturing and
a syntax street I understand that street For
example Canada sentences a dog picked up on
the porch So now for the same kind
of critical the first level passing and then
take it out they are these parts of
speech tax I have known around And a
second never You can combine these to come
up with business For example Departs combines together
to form a non freeze and then on
the couch trying to get it to follow
Prepare the shoes please and saw You can
combine all of this And that is how
you can help with the speaker That's the
constancy mastery I s office to do this
So essentially the same software that I showed
you online stand for portability software You can
actually download it You can run it all
your sentences and also covered with fleas off
this time Yes So nice fighter is that
once you have these please go is to
really look at these prisoners eso MPs and
nonferrous PP The proposal says BP's about flavor
and saw So you can just look at
all the non for business And then I
mean if you if you on then what
are there You know what I got a
spark to the non fist and because your
liberties for example the part As for this
basically mastery not not all of them are
reasonable It is like that is not really
something like a Houston face So what you
could do it serially applied like mutual information
thing on a perfect That's part All this
stuff that I don't know about this coming
apart you know usually comes with many off
them and therefore peanut this one and say
look like but on the kind of friends
you can get by really looking at these
NPP pr repeating That's another way off being
passed our bank closed addiction Okay so uh
yeah we have a little bit of a
writing off this stuff Eso the stock off
What is an LP that we talked about
what it send it I mean the really
big and start processing it It's basically hindsight
Francisco may find versus I'm writes from it
on Then spend them are processed themselves that
normalize them are nor kiss them and sort
Uh so since we have some anything Lawson
the pick voices descended vision This is actually
going to be pretty interesting So I remember
the example So I want you buy place
for most And the point is that you
want to figure out which most So how
do you know that many ideas Would you
go back Of course You have a dictionary
I want a picture The content to take
a chance to set off office All words
And for each one it contends many It's
our meanings I think it's been done in
simple English I know it has meaning of
all the senses of the good So for
example consider this I'm going to withdraw money
from the bank now Bank has it is
two cents with this popular one is the
bank s everyone velocity one bank institution Right
I say Say back huh Yeah These days
people only one bank it So I says
that Yes Is this the second having talking
for years Nobody talks that way about the
important thing weighing prosecution everybody way coming back
to the example Oh well we want todo
the studio We'd better look at the luxurious
years back has two meetings One is that
insufficient financial institution So and what we want
to figure out what is the sense in
which bank is used to hit They figured
out between itself a bank I thought the
financial bank a place here but we want
to do it automatically Way do it automatically
is the question Okay Now you think that's
the context Twice If you said money What
Yeah you have been trying money water but
because it was probably money like it on
the money is political bank the best that
money and bank You'll be able to make
some relationships of it so I wouldn't make
that relationship probably based on the description in
the dictionary So the train eugenic I can
sell meaning for every sense of a particular
world It also contained sometimes example usages So
example sentences and richer This particular sense can
be used it and some actually have done
him synonymous That's how for something far less
hyper names and saw So using this biggest
kinds of rice and comparing with the context
can sell off Say which political sense is
being used in this particular sentence Uh yeah
this one So this talk about t approaches
to do this stuff right And the very
limited which is based on what we just
discussed So for that let's look at this
example that is one example which sources used
this like first did that in the evening
flight on Then there's another example So like
himself a Sept 10 grand The pain of
the sentences used sons as on both cases
the world In some cases the particular world
is the world Some cases it is a
knowledge and so on It is easy to
December rate in that is but the physical
But the sense is that different Understand Here
it could be like self denial says Big
Fast Use that lunch my food and talk
But in this case itself a sick that
also the region ourselves that beyond stuff of
that sort would be the kind of picks
So what brings the descending Russian is Teoh
list for everyone Such a possible objects for
the group saying That dictionary also had set
off all festival objects with which this world
the definitely scared the star birthday That a
kind of foods in the sense fruit of
breakfast means Uh except on this one of
the stars with sickness are legion and a
few set off objects basically saying if you
can exhaustively inhuman it are human structure of
the world In a sense on the office
of this group they can use that really
Don't disintegration easy What says that if you
give us like this white cells that region
within remind really you would say that region
is prevent in the political sense off the
world itself on before you would say that
this stuff is really vital essence that session
But the problem is about this one to
work You'll have to come over the disdain
which you see a second objects that go
with particular work with this particular themselves which
is not easy to do its physical very
time consuming This stuff a lot of things
So there is nothing to do is to
really look for your next words So let's
in fact look at this example on bombing
to get cash I want to figure out
which make sense of the world off the
ashes being used So you want to know
what they're defending Vision for the cash So
back to the city I could figure out
the three senses in which the right ash
could be used But in fact ash is
also democratically three He's off the family the
definitely infertile park and three branches are also
called ash trees I was also with you
uh just left Then you buy stuff and
the person is the warp sense because something
in crashes also ash I mean he asked
the three or whatever the ash peoples and
whatever basically can working something through Yeah absolutely
Ashkenazi name of an activist That's pretty date
So in fact that would be a poor
sense here It's just that it doesn't exist
in the dictionary That's right Yeah you're awesome
And they often active s with that way
we discuss this has to do This is
this goes back to the other kind of
problem with Gandhi and struck the same problem
When we're talking about common words it's going
us a subdivision problem But if you have
proper names it's call this entity linking problem
after the problem of the same You think
if they don't talk about divorcing couples together
I mean s example usually in a pretty
linking worried people will say that they valued
and give that speech So now the problem
is typical Which Gandhi is a problem on
the blood booth I can't It's an interesting
thing I mean if you really see this
as a good venue of the story are
food and actress huh The baby on particular
which offer senses is the right since there
are three possibilities and you choose which one
I think as we discussed we look at
the context and the context We're gonna see
if he was like Bob bobbing and through
So now if waiting if they are this
unique meaning toe willing bumming what we get
do is to really figure out which of
the sense have a have a nice or
laugh with the context You find that this
one says but they just want also bombing
So then okay makes some sense right So
basically sense to it's quite created Why since
one And since we don't have anything like
running or anything like cool Off course We
don't get a lot of the stuff words
like on being get because that way they
don't care about this work But you get
about involved in and for the But then
I mean one can One came just got
said this is the context And then match
each often and then that Europe has a
good match is the one that wins So
in this case since two Goodwin because body
case okay here this bombing stemming is one
thing that happened that helps here the bombing
in the famous right Because when you stand
them what happened Become But wait So what
we can do is to go back and
look senses so about traditionally you see what
I can tell me And then he was
like really create something called the context Value
says that now contact is not a small
attack for the Texas does that That thing
the holes in the in the same sentence
So the chance as to how a lot
of context people back and I'm no match
with this contact And the sense won best
in there is not much much I mean
if you will see that Not much off
the useful words which will match here But
if you come back with this sense to
understand the context you see a lot of
much I mean the father which is a
combustible booth again if you look at the
size three it has are any match with
the context Basically what much of the context
is the one that Vince And in this
case again Stewart is the one which will
be used because if you have a sure
here and there was like following and stuff
if you are back in your funding through
Andi that's the reason why that will not
much in the particular case okay for the
last far worse since the 70 question is
that based on the second lap next one
is very simple Fictionally you're not just have
words and their meanings but also categories in
the sense that the bank has two meanings
one of the World Bank and the on
and in the location from which you can
withdraw money and so on And then you
also give Apache So this practical sense is
used in the location sense this particular our
World Bank using the location since all this
world is used in the you know finances
and saw So imagine that onwards Like I
think this Interstate 10 broad categories finance location
helps So in that case what you could
do is tow example Now this sentence the
money in this bank partisan interest off person
animal Then you want to figure out what
this event meat is it the location Bank
are visit the finance for us It is
the time for do you want to pick
it up automatically So we would do is
to really reflect the dictionary traces that two
senses one location since end up And then
we look at all the rest of the
context Money impressed any useful words and back
to the dictionary definitions Go back to the
dictator Get out what I'm senses Money is
used in the South That money Well it's
one for finance When he's not using vision
Sense s such money might be a spot
of the census but you know thes two
interviews in the finances I used to I
mean the animals used in finance and interested
in finances and saw so and you see
the point extremely works more for the finances
Dollar then publication since And that is well
again In this case I mean using this
kind of categorical dictionary You can really come
up until that We'll hear bank of used
in the finances Yeah I mean dictionary is
easy to get So essentially all of us
have actually that probably so difficult to trace
also exist online So you can basically goes
down with a dictionary say Vape store Merriam
dictionary can take it with food Exactly So
dictionaries and available like this This kind of
dictionary is where there is also this category
Mark is not very common So that was
a good up If you are using a
limited doing using for a smart woman I
uh you know these words are me and
Doug Owens assistant jobs remain Order the the
stick that demand that you can actually bring
such a thing I knew the last weight
loss of the medicine perfect Essentially see if
you have so far is a text which
exist in two languages For example parliament presidents
at every level in the various languages various
national languages Of course it is available in
Hindi and English but about seven Evelyn biggest
I think Ready for the conveys Indian languages
So the same sentence is transmitted by someone
in a Celeste Talk about just English and
Hindi So it's in English and also in
Hindi Um and say Summit of the parliament
Confirmation the money in the bank that you
have any crystal feet percent The nice part
of old boy senses is that the same
sense is nearly cost negative for the same
word in the language for example considered a
true sense of the World Bank making him
pay for one You will use the problem
in the office at this point but the
one you will use uh late for people
Most off your work will understand Hindi in
another language Well basically trance like it to
a very different would so basically saying that
the defective your targets in the translation it
is very useful It is very easy Then
difficult questions is being used So having workable
senses in one language will have distinct class
delicious in a living language based on the
context in which it is used the constellations
can therefore be used that included picking out
places Okay so quicky end with passing So
when he talked about possible So it's not
really a big game No So passing visit
Because two types of passing you could create
our fast back eventually a god big by
on the part of the expensive mastery So
now you would ask in which applications such
faster that Houston would have been the past
Any idea I wouldn't create some interesting Africa
cannot open any any yoga postures Okay but
you're talking about a natural language right now
Okay so that's not really an application huh
I would create a fast to transmit data
Uh I got it passed feet but then
what would you do based on that Yeah
it's Ah that's a nice one Any other
got a bald dyke But any any other
applications about the past Why would you want
to be in the past three I'm not
asking you would be one So always a
very difficult question to answer But that's what
work if you want to think about it
But what would you do it if somebody
can feel that a good many seconds I'll
give you a pass Street What would you
do with that use that to do anything
useful but speech You already I mean you
already have You think speech tablets I mean
you have been and I passed on the
street is just one year it will help
in sentiment And this is this is one
So we also talked about saving face and
everybody back They change could help you because
the space is one of every just It
is in this statement helping sentiment analysis and
how so The point is usually a particular
sentence can contain like the sentiments For example
somebody would say the least Block was awesome
right The cast was not very nice you
know Another aspect was not very good They
say like one sentiment It has two sentiments
In fact two different aspect Now you said
that there are two parts to the same
things You can basically just walk past me
And then at some point you'll pick it
out to the left part of the And
if you do second Thomas is you can
actually see that the left left power has
probably partners anyway right Part of the negative
Same doing And then you can say Oh
the stadium has who sentiments and therefore it
must contain prospects on def forgive defense sentiments
for the chest So that's where these things
are useless essentially sentiment that this is one
example But then many of the examples pasties
could be useful So for example if you
bring up you know somebody vision and they
were expecting someone Addition Strips of innovation You
want copy and past tenses but basically we
copied parts of a synthesis comfortable Parker Center's
from there and so on and then combined
them nicely Form automatically generate sentences so I
wouldn't do that You do that by anything
you know Units So basically you want really
pick I don't think But you think this
is a unit because this is a papa
groceries and then the bill will face can
be combined to the novel is that's what
you know from English language on that that's
where plastic abuse fielding abstracted somewhere addition you
need to be in the past three The
Vegas uses very distinct of useful and then
not only are they talked about dependency analysis
So if you want to really think about
subject robotic I think for example in this
case what is the world on who put
the thing the market to think that for
the buyer of the subject and therefore it
has that ends up dependency Now on the
one thing that I talked about with the
doc so that was the dominant object the
object in the club symptoms So they could
This stuff will be useful in that this
could be useful You want todo fax from
a news article Ah fact is nothing about
the subject Weldon objects factors basically so So
I also want things like some I think
labelling so that is a thing called gullibility
Wanted to go who did walk in that
sense it's a useful effect extraction from a
large purpose Somebody give you you know you
bring in a new country and you are
saying this guy that you want to sell
something in that country you want todo so
say you don't know anything about that country
What could you do So if you have
access to last one year newspapers in the
country you can run the studio automatically pick
it all facts and then think it's did
that white and so on That could be
really useful Britney understand the a lot set
of documents or someone alive That's uh But
this is just more detail on the first
off dependency structure says dependency that are finally
in nature So they have the link between
words So they just between Russia and they
just describe the relationship between between the between
So essentially it have ahead in the head
off the front of the door on uh
depending dependent But also useful Passive voice was
actually centers of subject pregnancy fashion sense of
deposit dependence the best on the dependency You
can figure out how many said that that
was how many possibles so that we discussed
already So that is all So that is
pretty much the end off literacy What we
discussed Hopefully not somebody last you know you
can actually say is I was somewhat about
beating So they talked about what is a
help in what's our fast fall into this
big So we talked specifically evolved the basic
models that are required to build any big
NLP system so open innovation normal relations stemming
sometimes segmentation of unify with after getting out
sentences flavor identification What's is this an immigration
on a little block passage Yeah it was
a large top It's the last media So
essentially like I mean uh many books on
an empty essentially the next things that we
could state of discuss after this would be
like popular sentiment Analysts are really good in
the detainee population I think they hold on
to some of tradition And so so for
the story of stuff with that make its
political analytics So if you basically want toe
you know so analytics is envious Tedious as
a disgusting I think I can go back
to the first State if you want the
analyst and signal for me What percent off
the customer business making your They called it
was going to get that meets and then
figure out something timeless is on top of
them That's basically a stick example Now I
think this necklace start making boosted just his
applications that use something natural language for example
It's looking died of your application is probably
no noise looking here but profited comics analysis
So let's say you have uh basically allow
discussions between the power and the pilot and
the ground staff is all recorded on There
are using speech recognition systems Now I have
up in this bag If anything happens to
the plate you can always go back to
the recording and then do some safety Meantime
listen sticking on what is going on A
cross accident on he said Anything human that
profits within it Tom Analysis can be done
Lived on these things So if there are
based on these charts blocks you can go
a lot of analysis in that sense in
various applications Russian video analytics No they thought
the boat sentiment the festival interactions user profiling
in e commerce and retain Looking at what
kind of is you're putting in more things
in the first Nice for you if you
can go natural language processing and get out
of that window Li Bai's more off on
my photo Robinwood off various such application What
begins to start if it's okay for the
first time Big What if their spelling mistakes
right So are fattening is usually very good
at correcting spelling mistakes Why Because they have
learned from previous uses no small message and
then you have some spelling mistakes in it
Sure I manifest many mistakes today There are
switched off my experience direction because uh you
can install them on your machine on they
make you have simple things like two characters
on the typewriter are People are frequently have
changed so those are the perfect because spelling
mistakes So the first Finnish people make spelling
mistakes and then the ones we saw importer
are people make spelling mistakes when this very
difficult ones like Mississippi on the movie I
and so on And then they have those
Nothing's in family See in the model no
makings off them in your in your now
for the second bush it like love it
with that You should start with this kind
of thing Are being packages good So just
play for Houston package Read it so I
mean everyone bizarre jobless You connect Big Nick
Uh just look at what is regulated The
I d Any eat meat Yes Investigated gives
you help fight for our party and package
And then there are many examples which are
typical in that fight So the examples can
help yourself are going Booth thinks I mean
nice examples discussed What would you do with
the impact that's that one should wait on
that he could run it on as many
Azumi document as you like what So the
Democrats you everything thats off of a second
And I was since this offer boots past
please which which we did not in the
in there which we've not seen that huh
But I I did not have the bride
five but this is going to Stanford Oh
when I say usually documented minutes Ah text
document It could media also but you usually
can article picks before doing it X tactless
is I mean usually expired Txt file Yeah
Any of that quick questions Yeah Sorry I
was found not more attentive to this part
of the class but I don't know I
mean that that has something to do with
the male Probably like really a classroom But
it so so I mean it depends on
what application you're talking about So there are
various reasons why people switch context and various
applications For example of inviting a new story
I might talk about something and then six
to another topic And then we'll talk about
the first topic again and saw so that
things got topic margins which it did You
know that the first paragraph us know the
world's you know get much from every day
So sometimes comically can be Laker appear specific
commented like such an and then your family
In between I started about actual match and
I go back to the topic models which
help us to do those things that are
very a ticket Basically they're not like it
but they use some kind of a job
So assuming it thinking all covered and somebody
screaming So if you have too fast in
your mind you usually I mean say for
example are looking up like from heaven about
two Bangla right on between Your voice tells
you go overseas for my guess in the
right way Basically such for searching for cycles
a lot of bang Olympic before you complete
that such a start setting So is that
going into musicians But um unusual decision is
ah homogeneous task But just because you have
so many things in your mind to do
at the same time you want to live
in that sense and that is enough for
me I mean there's no there's no published
stuff but there are significant hope really You
know tomorrow this internally So I figured out
that this was one of them is another
task and then say that this part of
the thing that looks intently actually these musicians
that is good work But there's just often
assisted Yeah Yeah Okay well that's a good
question Rates the stock a prediction Did not
uh on the box office to do it
so far Figure Salafism You know it is
interesting that food is off Bring soccer Prediction
Oneness context Insensitive In which case the symptoms
that since thank emphasizes on the other is
ah context sensitive In a sense if you're
basically if Elektrowatt really really badly and you
go and then one guy sister about that
soup all right on you know that says
sarcastic innocents And that has to do with
context because I didn't attend their faith and
then then then understand that the sarcastic the
person would think it was any support thing
So some approaches to are still in research
And there are some papers that some people
have used the French TV city dialogues and
go sarcastic comments from a and then try
to real photo mostly classify and say Oh
this is fantastic this is not And so
there's published often will help you I want
you to go when it comes to sentiment
Timeless is so You know usually you know
sentiment timeless is best in the world is
contained in the sentence But uh it's really
nice So sinkings from one of our perfumes
magazine So the same time says so If
this is your battling pregnant then you've been
a very only exclusively I did nothing Those
shut This is such a sarcastic sentence Contest
member thinking Well if you consider that this
is they get the same thing in seconds
Uh it is really important when you don't
visit will pick us It's difficult to solve
food that it is Booth fister that some
people and questions a quick questions Yeah So
evocative efficiency aspects Burkett Practical You have to
vote So it says you have to really
look at everywhere UK frequency and saw Yeah
Yes absolutely So that the answer is yes
You still need to I mean I know
there has to be stuff done efficiently but
you still need to relocate Fictionally is the
dictionaries and making those count and saw It's
just that you have to really bring the
buildings which are faster which nearly brought up
in six Really creepy Um this next time
that dictionary that find me because I mean
of course there's a body of Webster's dictionary
just which is the start of the people
you were supporting But I understand it actually
because if you want to keep coming in
and as far as an MP s concerns
you just don't care about us which existed
Actually it's on average in English Fiction it
in whatever jurisdiction inevitably backwards But there are
many warm the keep it Let's attending members
also did not exist It actually But if
you think you need to know about it
they're bigger actually Using off completed items for
the people you are You got everything You
dump everything And then what do you use
this also as dictionary they have to keep
going initiatives like this that is most under
Actually that one let me not myself kids
But I don't keep it in my car
Yes it's a good question So it's nice
that you asked so that I can talk
about because of some living there So So
a child that's right I put it in
the last fucking because it's not easy to
do it in a dented equipped So in
the sense that if you are such a
back hello holiday weight one way back in
a statistician's attack points out off writes one
which really just engage you in conversation E
People talk about doing different in your pocket
Us people Usually you need to talk So
11 kind of Assad which just engage in
bond position That's all it has to do
nothing and it's just gossip about it in
public This again honest information check what's you
basically give up some useful information the information
usually domain specific But this Chatichai parts are
the gossiping ones are the more interesting ones
And there waas Microsoft's first words that come
in the next video Chuy Parts is that
you know if you ask it thing with
back London before you eyes that how do
you know that we can build and saw
So now there's no obvious cooking is basis
that but so what we could do it
is to really gather all the critical positions
So somebody oh I think I'm gonna be
oh so busy place can actually be used
to play back I mean if if somebody
you you long that got back saying I've
been But it's all of us that don't
a lot of people say nasty things also
playing back late So and then if you
love those master things then you know the
results of those nasty things So in a
test as to fix So that's what happened
When Microsoft lost the booth people started asking
nasty questions So basically this talk of asking
good things make you know what you think
of blacks versus whites in the U S
A And then now I'm in the newest
match on You know what You can get
some pretty this one day I get all
kinds of chastity and that's what Microsoft's Jack
Dog would also see It's not that simple
that I'm just simply thank the takeoff this
conversation So which causes issues that as Microsoft
you cannot repel stuff nasty stuff and therefore
might take back that But that's why I
mentioned it is one of those things that
is still in the set and there's no
commercial I m and practically similar So it
is more an MP for natural language processing
lab You have a brief high level overview
of where this thing is going and then
we can be for a wide where idea
of things it is not a dis perfect
needs a lot of refinement Simple simple things
like color and color can be a problem
people are trying to do would improve it
However bulk of the work is happening in
the English language So we want to If
you guys want to do something in your
local language or something or local dialect that
is good for the startup completed their question
is we're going to pay for it That
is obviously the end question right now Having
said that today I actually did something interesting
I thought Let me send you some um
something called text blob So if see if
you can open this Web site I think
what's his name What's Okayama steps and put
it up for you guys today Now you
have to understand something that remember I was
telling you that there must have been somebody
who must have tried to analyze information and
words and store so these guys are made
like ready made libraries and indicated natural language
language The kit which has been developed by
Stanford University on day keep upending it from
time to time So if you're really cake
it is not running It's not because off
you somebody must have upended the debt Stanford
After doing some research you need to keep
that in my text Blob is something similar
It's a lot it so it's a little
bit faster than any lt K because they
don't have all the features But it has
some good features in it And of course
PC is the faster so all of these
things are there So when you go all
those outside the class and you certainly see
on day down on on medium or someplace
this being used you must be wondering Waters
work Did all this begin Talk Talk about
it in class It's nothing It's just picking
up the library functions on It has got
a huge score power to the corporate me
or to the corpora It has a huge
core path documents a collection off documents at
the back end on in the collection of
documents it has the rules is a word
These are very common down proper noun like
stuff like that All the things that has
got the rules are written at the back
over it So that is why the corporation
has go for all of them have their
own corpora So all of them have their
own core part of start it Now What
I'm doing in the beginning here is something
simple I'm just going toe kind of download
some files here so you can clearly see
it's nothing much I have not done here
much Just put in the usual files something
and uh some things I downloaded when I
ran into her problems because I was running
it It was not working So I was
Googling and figuring out what is wrong with
it for some Although some of the 90
K tool kits were downloaded from there Having
said that the next thing that that it
waas it is the pipes and library you
know for the simple ap I So I
put some commentary for you guys to keep
with you It performs methods and performs basic
and LP task Ah good thing about it
is that they're just like Martin strings So
we're not by 10 strings We're not by
since strings What did the Bipin Street You
have a string right Inviting you have a
string So you have a string in fight
And what does it look like You'll just
have this Not Saudi This is not working
or despite a string So you have God
It is up Book Gotta blow right So
the fighting string str string So that is
what it is If you do this it
can lead to a problem You might have
seen this It's so book If you do
this it can lead to problems What problem
will it lead to This is saying that
is getting closed here But it is The
loop is not getting closed So we have
got that So that's sorted out there It
has got side order text blow up here
And I picked up the 11 characters and
I said I'm starting from very basics I
know this must be very basic You have
done this 100 times but I'm starting from
very basic just to ensure that we can
go but on the same page So you
have got that and you can see I've
used one the strings have used an upper
upper is basically built in command that will
help you make make it up And of
course the next thing is to concurrent Nate
So it's some off You are running into
any kind of problems I have also told
you where to download the corporate because sometimes
without downloading the corporate men artwork So if
you're running into a problem in the class
right now then that is because the down
there the installation is not happen If you
run the court you will know if you're
running into a problem Okay so if you're
running into a problem then you need to
go to an accord up from 10 download
the corporate then we get to the real
world Real work The really work is to
organization What organization is to divide the text
or sentence into sequences So creator text block
object and pass a string with it You're
creating a textbook object passing us extreme with
it Call functions off text block in order
to do a specific task So you're calling
the function could do a specific task here
So this is my stream This is a
text blob streams have to be very careful
This is not a regular bike on extreme
It's a text block stream on Have you
heard of Jason Format Yes just like uber
Jason format For certain things especially if you're
doing Blockchain you have to put things in
Jason format here Also you can see this
is a text blawg and and that is
there and you can see I've organized it
So blob dart sentences So it has taken
the sentences that ever there was And it
has made in separate Tokars So that is
very very very simple Commands blah guard sentences
You can further break it down into words
so I could do that So I went
to the next level and I father broke
it down into words So I just took
the first sentence That is why everything zero
there If you wanted to pick up the
next sentence you could have it in one
on You could have broken down into words
Very very straightforward simple broken down into words
So organization off sentences organization leading toe This
Now comes the interesting part The interesting parties
we only want to take over the lounge
phrases we don't We are not interested in
taking out anything else but only the noun
phrases So if you want to take out
a noun phrase you must first and identify
the noun phrase you want to analyze the
who in a sentence Now I did this
I said it Lighten it A great platform
to learn data science known phrase Print MP
First of all can you please check and
tell me in all the world's announce out
of the old nose on all of them
knowns Yes No Which of them are now
So you're not knows Sorry Great is not
a noun right It should have been an
objective This shows you what I purposely bring
this example is because they shows you that
another day you're dealing with machines and know
that you're dealing with machines Somebody must not
her at the back and being able to
get to that level of granularity where we
can clearly say that great is not this
And it is this right So another day
you're dealing with machines That is why it
is speaking up some stuff that you should
not have picked up yet going forward it's
quite simple After that I picked a great
platform I went around and I did some
tagging So this is what The thing I
want to talk about parts of speech tagging
So in the part of speech tagging what
you have seen is it tells you work
These things are it as you work at
light and is is a great platform So
I've done there tagging for all of them
So all the tax you will notice They're
all capital letters all capital letters So anybody
can tell me What is we BZ mean
What does BBC me So when you run
this you get something called BBC What does
we busy mean So let's go global Go
back and check on the Penn track Where
are we Undertrained Cracked Re busy What does
we busy What third person Singular Present what
A third person singular present So if you
don't know the meanings you need to have
this ready Reckoning Some of them are very
easy now one singular or messed Let's go
to a few of them just to kind
of family rights How to read these things
So if you go back here you have
God JJ What does Jamie I mean It's
ah J D j d means objective Do
you give me inserted an adjective Don't go
back and check if what he's saying is
correct is great and objective is good An
objective Agree with you do Ah what does
BBC mean Let me see We busy Is
worse than third person Singular That is mean
Singler are means pruner Right So you can
see the singular and cooler platform is a
noun Learn learners verb again I think it's
a simple world BB global based form is
now on You can clearly see it You
can see science You can take the full
list I have left the full list here
as well Okay then comes is the process
of word formation added to the base word
So I've got kind off made the base
word You can make words out of the
base world So blogged our sentences One doors
Word word one What will this print Can
you tell me What little print World Dark
sentences one dot words one What will that
print in the first sentence It will Pinder
second word I will pick up the second
sentence and print the second World Think for
a minute what it will look So if
you do this blob dark sentences one It
had been zero Yeah picked up the first
sentence One means it would become the second
your touch and then it picks up the
second sentence You can pick up the second
word inside it and then you can say
Make it singular So I first picked up
the word dancer was helps and if I
want to make it sing that I can
say single Arise it Answer becomes help So
from helps we have gone toe help so
you can see it's fairly easy to use
It's not something that requires a lot off
brain poverty I mean I'm just trash talking
U office unless lesson When I was in
the class so kind of showing you some
of the basic things that you can use
In fact you can use some of this
for your for your projects You know a
lot of challenges that you're facing with Project
is in terms of finding data if you're
doing MLK under work if you're doing convolution
neural network kind of got image recognition Everybody
talks about object detection in some form or
the other Everyone was talking about object detection
The problem is that many of those things
are not easy to do I gotta go
added the mathematics Behind it is very complicated
for a six month timeframe of seven month
timeframe but one area that you can explore
is try to make sense or using machine
like machine learning Try to make sense off
customer feedbacks reviews Critica cones So once I
had a bunch of students work in 29
green before the elections I had a bunch
of student analyze the prime minister speeches So
they picked up the speeches of 2014 and
2019 and you can see the marked difference
in 2014 speech He talks about corruption price
rise a bunch secular attics secular country All
of those things with the key four or
five words on in 2019 it talks about
work The silence in the class 2019 Where
does he talk about Balakot Hedonism Pakistan That
is what he talks about So these guys
went and picked up all these speeches and
they just did what I'm telling you This
trip did induce months most once while chunks
took out the common words across documents and
sent This is what he is talking with
The keywords were found in all his speeches
concurrent across all these speeches Top five words
that asked them to find out Have a
very very interesting thing Because you see sometimes
numerical data can be misleading And I'll tell
you give you an example right now in
the class Why it can be misleading So
all off you know Saudi today it seems
have of having a bad day Every penny
pickup has no ink in it So why
you could go Why equal to five plus
six X right Why equal to five plus
honors make it via equal to 56 Yes
And I tell you that the R squared
value is 90% Okay Now looking at this
equation what do you remember What do you
think of off something you've seen on this
in this program Unsupervised supervised learning linear Riggers
right to think about the linear regression But
at the end of the day this is
the mathematical mortal But I Now the question
is what is the difference between mathematics and
statistics See That's the fundamental point here What
is the difference between just doing maths and
counting Two plus two is 44 plus two
is six express Too easy That's mathematics Then
what did the difference between mathematics and statistics
Statistics should also called mathematics Only Saudi Okay
starting from getting influence What is correlation What
Sorry So it is a general and here
You Okay So you know we are in
this You have been doing so much off
this stuff at some stage mathematics and statistics
starts to get blurred right I mean sometimes
you say I'm doing mathematics Sometimes you say
and bring statistics But trust me it is
a very very significant difference between their two
words Mathematics I gave you an example Two
plus two plus 26 right statistics You're talking
about this and what did The sickness is
Um that if you look at this model
stand alone This is a mathematical model I
agree with you This is a mathematical Mordor
Because this mathematical model does what it relates
Xto way right then very the statistical part
coming out of it Because if you remember
complete if you remember you just told me
in the last This is linear regression Yes
or no Alina and you and I both
agreed Coming from statistics So that s statistics
part coming for you Yes We arrive at
immigration a sample but in the very more
one more interesting thing You see all these
numbers that you have written on the board
here Five plus six Are this set in
stone or are they changeable Why are they
changeable How can they be changeable Because two
plus two plus two in mathematics is six
now I cannot make six equal to eight
Correct Two plus two plus two is six
Only then how God how can this change
Remember something that I we always ask you
to do Calculate what gone for this in
double So when you calculate the confidence interval
you say the confidence interval of this is
let us say minus 3 to 8 on
the confidence interval of this is let us
a uh minus 1 to 9 This is
what you say Which means that this numbers
are only an estimate based on the population
But these numbers can vary between minus one
and night It can vary between minus three
and eight so mathematically than mathematical numbers But
this entire model is statistical because the numbers
are not constant The number going toe change
the sample the change otherwise nowhere else in
mathematics it will go back to apply your
school days Nowhere didn't last nine or 10
we but I had so many equations expressed
by equal So they they send their Did
they ever any faculty ask you to compute
confidence interval in class 10 11 12 No
because we were dealing with pure play mathematics
We were not dealing with 36 in the
first place Now you are dealing with statistics
That is why each and every number that
you have has got a confidence is going
point by the estimate on why did what
he was saying today in the morning about
estimate This is estimate but these estimates are
all beholden to the confidence indoors Now why
is that point important Appointed important is that
when you're dealing with things like this like
language and all of that we're talking about
statistical models because we are also not sure
that what we are saying The system is
speaking up Is it going to be 100%
like I'm saying is it could be that
in some other mortal the word is is
considered to be no whereas is always a
work right is always a work so it's
always going to be some degree of uncertainty
when we're doing there tagging for these things
were going to always It's never went to
be now 100% guaranteed Now we are saying
it is 70% or 80% chance The word
is now so you might look at a
bunch of words and say you were a
noun without ah but a lot of hair
What is happening here So that is where
the statistical element of this whole subject comes
into place We are not We're still not
sure about how to break things down and
put it in place specifically because each of
these words have got many inks running playing
All of those things are there So the
displaying this comes before playing display so replaying
playing for all of those talented that there
and that is why we are dealing with
statistical model not so much as a mathematical
model That is the confidence interval behind each
one of these things Having said that the
next part obviously was to try and see
how we can take the words on how
we can polarize the world So just take
the word platform And I wanted to just
criticize it into platforms tying back to what
I was saying here that if you guys
are doing any kind of statistical model in
your capstone project than if you can do
any analysis next which can circumvent your find
which which can ball Still your findings So
whatever mathematics assisting your question is saying the
linear question is saying that the sales is
dependent upon Let us a disposable income sales
is dependent upon disposable income If you can
have any kind of English argument with all
this and LP at the back off it
it's only standings Your argument in front of
the client The client feel that not only
other remarks the quality did remarks also saying
that the mathematical remarks are also saying that
right That is the reason why we teach
the scores because this party already know the
spark You wonder you know but the important
part is to understand that it is not
by itself enough to justify sometimes many actions
because sometimes numbers and what the comments have
been coming are different So for example I'll
give you your own example One day I
had a one day they will just getting
the feed backs and we lose radio feedbacks
I was reading the feedback and somebody had
returned some comment I could not understand the
comment and I could understand the ready So
I said What happened How did this go
roll So I called up the program team
I said I can't understand the rating and
the common So I'd other comment is right
Other Anything is wrong One of the two
things has to happen So this person this
what The program team called up the student
to find her I don't know the story
in this book Soudan said I actually wanted
to say this but when I was marking
the person's ratings 12345 mistakenly market like this
right Mistakenly so data at times can be
wrong Numerical data can be wrong on when
you look at an emptied can tell you
hey you know what your new medical is
not matching with your feet The return Treat
about that you have given to the faculty
Okay you say Always a very good faculty
Blah blah blah accidentally you see giving operating
over that is that is your understanding over
And I think that's a very good one
Having said that that's good Having having so
then we move on to the next thing
Hey guys it's Ah 3 20 Is the
speed okay What Am I going too fast
Speed is OK Generally the students in my
class control the speed I don't control the
speed The only than Obama going a little
fast because all these things are a little
bit simple Just adding 11 command at any
point in time You want me to slow
down Please let me know And anytime point
of time you want to gold and have
coffee or tea Just let me know We
don't have to wait and four o'clock to
go and have coffee Okay Don't worry I
will still get the content on That's my
dog Okay So having said that let's look
at what is the limit Ization Go Meaning
So Lebanonization What happens is that he takes
the original world called running on gets the
world which we already know on We say
lemma ties it to over so don't keep
so they see the other important things is
why are you saying lemma ties be What
does he mean Be here represents the world
Why should it be by shooting with Emma
ties to a work Because if you don't
write me it could also be the limit
ties to something else What Because it could
be lemma ties to maybe on Italian word
in the Greek it Alex world which we
also have It is a birth off English
language what Shakespeare comes from So that's where
the issue is that you have to sometimes
tell them what you want Oh lemma ties
Then you have God in grams now and
grams are actually quite interesting because I will
be using them heavily for classifications today and
tomorrow It's also there in your internal and
external external project or you call it lapse
If also in that on then it is
also in your massive media major media project
OK so I would certainly suggest to you
that so we talk about N grams So
what N grams is very simple Before you
do that let me try to explain to
you the concept off and grab on the
board because this will be a lot easier
on the board and me trying to explain
it to you here So let's say this
is a book right So you have got
the first word The second word Third word
for word This would be calling And Graham
one Where does Engram one me Only one
word after tight Now you look at and
Graham to so engram over look like this
from this is an graham toe So now
the engram to is going to be This
is is Ah this is is uh a
book and I I could have made it
into trios Right You could have done that
also so that it works here that is
I gave the command for two Sweet may
Made it in Toto and Light and is
is ah great great platform like that So
that is where it waas on Then you
have God sentiment analysis Now comes the fun
part Saudi gasification problems So what we can
do in the classification problem which you will
see is that we will convert these two
words in tow numbers and we will bind
them together So that would have a new
vector And we will say this Victor Okay
Okay So now comes the interesting part This
is what I had been waiting for on
I wanted to get toe sentiment analysis Everybody
wants to do sentiment analysis in sentiment analysis
that are two parts one other two parts
polarity in subjectivity So what does polarity mean
Sentiment is basically the attitude or emotion of
the writer What is your attitude Are you
a positive right there or negative writer Are
you a good You do think in a
positive frame of mind You're thinking a negative
frame off my So that is what sentiment
is In addition to sentiment you also a
polarity and subjectivity So within sentiment polarity and
subjectivity is that on polarity varies between minus
one and one Where one means positive statement
in the negative one means negative one means
negative state So actually if you are looking
for somebody who was plus one you say
these these sentences are these faces that positive
negative it means negative This is this kind
of stuff is work The what you can
use in the capstone project so as to
speak Yeah yeah it is doing about around
calculation for that Certainly detailed calculation It is
taking some of the words which we want
Really good Great All of those watch it
is taking on Then it is making a
this thing at the back over Like what
if you go Yes opponent lifting subjectivity is
where you are talking about like contextual the
contextual part It doesn't do a very good
job Offered it speaking it out But it
is not doing a very good job Offer
it So let us say let me give
you an example of subjectivity So like judgment
where opinions were the first to factual information
So I'm saying this is a good book
This is my opinion Is it really a
good book Your subject David You know I
think I did that I think is gonna
NLP is a boarding class Somebody might say
sir and plays a great class It's subjectivity
right So that's subjectivity is tries to map
polarity It is very good at doing subjectivity
I'm not sure I'm not So can come
comfortable with any of the three packages because
again the way it has been designed in
the back and algorithm which is being used
it is not consistent across all three All
three meaning And but it no it has
been our points So it is by grants
and try grams it restrict my general by
Greco's individual words It has no meaning right
You always want to look at by grams
is our is great Are Greek are great
will have more subjectivity armies more People think
that this book is great is great means
only one person think that the book is
great that any other questions No it's a
great so anyway so that is where we
are I try to do that here on
I was seeing sentiment off 0.8 subjectivity off
0.75 So let's say that it is fairly
ableto become the subjectivity and the polarity on
duh it says polarity 0.8 What does polarity
of 0.8 What would it mean to you
Positive I'm already saying Night like ended a
great platform to learn data size It has
It helps community blog's hack a thons discussions
except we can see polarity 0.8 which means
that the statement is positive mostly is a
public opinion and not something that that is
the factual information no one has ever said
light in this perfect light into the only
place in the world where you can go
and learn data size Having said that if
I either even by a using the word
as light and we don't know what again
is because that's my fault So that's what
it is It's I Unless you must be
wondering why a Which was in a word
in Titan That's why uh you can also
spellcheck function here so you can check the
spellings You can look at spellcheck function You
can to ah two words Spell check platform
got spellcheck A On the next very important
thing is about somebody off text somebody of
Texas actually quite interesting That's going see Oh
this one You mean spellcheck equal to one
right So it's picking up the food and
saying it is right 10 There it is
what it is Okay so we're kind of
going through this in a nice way Neat
manner on then I have got some stuff
here And here and here Let's just let
us see what we what we want to
do here Ah letter school and look ready
to go Ready We've already we go If
we do the Ingrams you did the liberalization
Okay fine Creating a short somebody off text
You can see the somebody off textures here
the text is about You can create a
short somebody from the short somebody everything Nursing
book work tagging block tax The keyword says
that texted about platforms industries platforms again Second
seed has come back twice Platforms come back
twice Communities forums So it has come back
plies the word platform You can create short
somebody You know where this is Helpful Any
idea what This is helpful One place in
your workplace for this is helpful So one
of the areas that I used to find
this kind of stuff But I wish it
were there when I when I was doing
this So I so I used to work
in Hong Kong on The problem is that
I used to go to it and a
lot of conferences on behalf of my own
fix That is to give me a lot
of research material and stuff like that And
most of the material would come either hard
copy or they we should request they will
send soft copy off it Now who is
going to sit and read 500 pages of
research material Because every week some conference is
happening in some part of the world Now
I'm not going to keep sitting and reading
it right So one area which is helping
it could have been helpful in that insert
off You writing so much text you take
the entire text from the acrobat reader or
word document of the evidence put it in
there and say what other keywords are keeping
persistent that like please give you some sort
of somebody Doctor Aereo interest or you are
not Now if you already know you're interested
of course read the full thing from beginning
to end But if they did not something
an 80 off interest than you could have
done that But I didn't know partner their
thing And I didn't know any particular NLP
any of this in center things that only
were planning to b just doing normal regular
work after Maybe so Now I feel that
I fired known that I would have saved
a lot of time because large document but
I'm finished reading the tent past said You
forget what is in the plus deposit and
I used to have a boss who was
old school So you would say I want
to somebody off everything which has been drinking
So I used to keep light like I
used to every powder I used to have
to make somebody on the side because at
the end I have type it down and
make Make it in 304 100 words he
will say because he was the chairman he
said I have no time to eat so
much but I do I want to know
everything So just to be a pain if
this had been that he did like more
faster to do right One interesting addition on
the on top of this that you can
do is Web scraping What is that scraping
Let's scrapie websites You can scream the date
out All the textual information from the website
FBI I just be careful You don't know
become illegal so if they find out you're
doing Web scraping on some of the websites
that I can be blocked It was not
illegal 34 years ago So when used to
do this class for just over 11 million
analytics usedto boy do Twitter They called all
that sent all the messages and used to
analysing the class in the real time Right
script the holding and light in the plants
in the real time But now it has
become illegal So you have to be with
a little bit careful of how you'll stack
But you should So that's when you're doing
this kind of project Just be careful about
it That's all I was trying to say
on then One of the interesting things here
is that you can also check the language
off a word so you can pick up
the language of I picked up this words
here on I picked up this world made
it into her text globe on what does
it just It is Chinese and I said
Hey let's convert this Chinese to English on
Therefore I did count I converted to English
on what is the English translation of the
Chinese Worth This is school right This is
absolutely cool Everything is going Well what happened
You want to write even bigger than this
Now I think I'm fine I think I'm
fine now You guys can see right at
the very end That's why Both Okay now
let's start doing some classifications more this year
which is going to be crazy work It's
okay let's do But this is what all
the basic stuff I wanted to cover with
you would wonder to just go through Single
allies brutalize pickup were do some basic sentiment
analysis and stuff like that But that's not
what this gospel about this last last time
I checked was statistical and LPs to know
have Northern industry districts in the class So
I'm speaking to wonder if I'm picking their
own class Having said that I created some
training The interstate Now in this training get
a said it didn't created I picked it
up from somewhere But some interesting thing about
this training data set is that you have
got the sentences on next to the sentences
I have used negative positive negative positive So
these are assigned not by not by anybody
but the user So I am the user
I have created his data set So I
was signed I have said Tom Holland is
a Taliban terrible Spider Man Tom Hollander This
terrible Spider Man in the negative sentence You
agree with me then I have got are
terrible job are true inlay miserable for me
again Negative The Dark Knight Rises is the
greatest superhero movie ever positive So I did
this on my own Just took some sentences
in a classified damos assed good or bad
sentences on Then what I did was I
did the same thing on the testing site
under testing side Also I did the same
thing I kind of get that there as
well takes provides an England classifier on Finally
I came to do what is known as
took back on I went into text blow
classifier than I created what is known as
naive based classifier on I took a naive
Bayes classifier Have you by any chance heard
off Now you based Yes You know what
it is We'll get her Tell me what
it is Okay You're very close Very close
I think Almost like to the right point
Anybody else would Ward is naive Base Have
you done it first What am I asking
a question that should not ask See Tell
me one is late If the nothing that
something had not been covered then let me
know when I will cover it But so
naive made What is my race Zero correlation
between features Okay what is rigid independence Which
is what he will say Yeah but what
is nobody's Why do we use naive ways
I think you were taught and supervised learning
Right Next week I'm getting supervised learning in
a Emelin Google So 90 days Any thoughts
Very good Answer Probabilistic model based on base
Todo So how does it work Like what
What is happening inside it See Largest circulation
If I ask anybody they will immediately says
that's a classification problem Zero and one That's
where people will see Yes The minute I
will ask you about regression Simple linear regression
A shorty on the board Everybody would said
Send a prediction problem X you give viable
Predict So given a border already that we
can occur But then why are we calling
it now You bet you could just call
it from base now So what should I
write on the other side right So this
is how based Children's books Simple based Toto
on the Refresh your memory a little bit
You have God to sex and B on
this is a dissection We right So what
you're writing here is following p a given
B is equal toe probability off intersection be
divided by B B on then you are
saying be be given A is equal to
probability off incur section B by P A
on then finally you are saying the following
that this implies that probability intersection B is
equal to probability off egg would be be
righted by B B Uh no no multiplied
by Phoebe Saudi So it will be multiplied
by BB on You're saying that probability off
in the section B is equal to B
B give in given a into probability off
Yes this is what you're saying on Then
you're saying that these two things are equal
and because those two things are equal or
do we end up doing probability off a
given be multiplied by probability Off B is
equal toe probability off be given a multiplied
by probability off Yes on then that gives
you probability off a given B which is
equal itto probability or be given a multiplied
by probability off eg divided by probability off
Yes right So this is what is no
the simple based serums And what's naive about
it where you're saying what's now you're voted
So think about it for a minute Works
Now you're bored Okay so let's see how
this will work I'm new if you have
life insurance contracts life insurance for your life
but a few some off you life insurance
So as life insurance when inserting and doing
your life insurance pricing policy I used a
falling logic I suppose you come to me
and say that I want life in Children
between the years 50 and 60 The person
should be first alive delayed or 50 then
only if something happens with the person given
any life insurance could it So what is
the condition that it's not as simple as
community between 50 and 60 Probability is that
you are going to be paid conditional that
you were at least at the starting off
the contract A life on the contract starts
at wartime ego 50 so it's no more
a simple probability to the conditional probability Just
like they say this condition to you Getting
very good marks in your plus two You're
going to get admission in tow Call it
You did not Just passing by the precondition
toe that so conditional probability is heavily heavily
used in sequential by models because everything is
a condition The sky is blue the sky
is blue is very easy for us to
say But for a system could detect that
has to be some probabilistic model which tells
it that when you have this kind of
sequence the sky is it should automatically point
toe blue It is not talking about the
sky has a risk I had a rainbow
in it It's only talking about the color
of the sky So probably this is important
based on time is important Now that you
understand how this based on works just wanted
to kind of draw it from first principles
for you What we're basically doing with the
fall away Would it be okay if I
ride the fully be a baby is directly
proportional to we be given a in due
beer Would it be okay Yes No What
have I dropped I have dropped the denominator
because I've brought the denominated My equality sign
has become inequality saying Got it That is
one of the important things here Now what
I will say is be a instead of
B I put in multiple conditions Tita One
teeter toe hit a tree and explained that
doing a minute to Taiwan Tito Tito three
This will become work Tito one teat Arto
Tita three Given a into probability off So
every place being sort of having one condition
How many conditions are now God Three conditions
and what he was saying Waas that you
can further replace it down by you saying
BTW on into probability Tita too into probability
off Tazi given a multiplied by probability off
a How is this possible Zero correlation and
independence So the conditions are independent So because
their independent you are able to do this
So that is why you are able to
take because their independent all the three times
are independent But they're all conditioned Only you
are in a position to write it like
this And this is the mathematical model that
we will be using and find a job
full problem for you to do in excel
So whenever I teach these kind off statistical
models I prefer to do one problem in
excel with words and sentences where you understand
what is happening Our friend here can do
it in one second but we have to
understand just taking same problem What did the
meaning Millions over into entry understand what is
inherently happening So I'm setting the stage before
but I know it's hitting four o'clock And
I also know this 100 day today So
we have to have coffee so I'm setting
the stage for that So this is where
you're talking about naive base and we will
walk out to problems in the class today
in Excel before we do it in Buyten's
ready to understand how it how that part
off base totem applies to text Um should
understand how it applies to numerical data because
it has been covered where in your of
course supervised learning Okay so here what I
would just finish this often than take a
break here What is happening is something simple
I garden I based classifier I'm training it
up after training it I am saying the
following training it and finally asking you to
do some simple things for me I said
You know what Classifier Show something from shoulder
top three informative features So what other dog
free informative featured It is showing me the
top three informative feature that is showing is
that it contains is equal to the water
This mean contains is equally true If a
sentence contains is contains means should contestant intial
containers if a sentence contains is if is
this present in a sentence in the next
sentence it is a contains a false means
work A should not be there in the
sentence again you can see terrible It should
not be there in the sentence So what
it is saying is that if that be
the case the positivity negativity show is 2.9
is to what So you can see that
the positive sentiment if this is this word
is there Is there the chances that your
sentences a positive sentence as compared toa negative
one If this word is nor dead the
chances that just intense is a positive sentences
1.8 If this word is not that it
is also going to show us 1.8 but
that is what it is doing on Then
I just took us Just made up a
word right here in the lab was typing
this down explode The weather is beautiful classified
classifier and it tells you it is a
positive sentence and pretty much they ended up
ended this classification problem there itself However it
is not that simple On I will support
the pros and cons so you can see
the pros and cons of some of the
things which I do NT K and Pattern
therefore makes it simpler for bigness by providing
an intuitive interface too energetic and if they
get a lot more complicated Language translation detected
powered by Google translate not provided by Spacey
Large slower compared to Spacey but faster than
an lt K does not provide feature dependency
Parsing work to vector etcetera were to what
you learn when you are doing Ah the
next course The next course is sequential and
lt very well Talks are talking about how
to use deep neural networks in order to
do work to wreck translation So this was
something just terribly overview about looting Text globe
about how kind how different types of text
can be analyzed and we can start working
on when we come back after the break
We will then talk about something more intense
and challenging about headline news analysis some more
statistical stuff So this was just to get
you started to get started on the journey
off NLP having said that couple of things
off that is a file which I have
given you on That file is known as
news headline analysis So just open that file
Okay You do get this fight so that
the file buttered countertops saying news Headline analysis
Dorji DP Yes OK in the four dirt
or not in the folder I think it's
not in the folder in the link provided
by Sukanya There is a massive CSP file
which has got 2.7 million headlights Yes You
would have been able to see that Israel
So can I Would have provided that link
to you We were struggling to upload it
So it s so I don't know that
she has provided a link It's it's dead
Is it there Yeah it's that search day
What I want you to do is first
download that file or somewhere where you can
access it and sickened I want your door
read the file in I want you to
read the file in because they're reading off
that file The database to read It will
take us half an hour So if you
study set it up now then by the
time the break ins and we're already to
come back and start weaken do something with
it Okay so you want to go ahead
and read the file in It takes a
huge file yesterday to Western and I was
testing the files before coming to class It
took me two hours I was so frustrated
because I couldn't do anything and leaving the
laptop would have made a great social structure
down on its own So I was like
sitting there till it was 1% Half a
percent on Before you do that put it
on hardware accelerator deep You because I learned
that this is much faster to do it
just kind insisting so go to runtime settings
We're doing time settings Go to run time
here and intern time We will see change
in time type right So said it required
a GP hardware accident unequalled GPU Okay that's
important That just saves you a lot of
pain So once you're done with that let's
do a quick class recap of what we
learned what we didn't learn and what we're
trying to learn And then let's take a
break come back and start getting into some
really heavy stuff In fact the news analysis
If you'll see this while it has got
BDP Congress Ahmad me party everybody inside you
So it's a lot of fun you will
understand There's a lot of fun to analyze
the headline What did they say When did
they say if I didn't say on and
then if you guys are still time permits
and you guys tell me you are still
charged or produce statistics then we will take
this forward and to do so solve to
excel problems on that Okay but it's completely
up to you guys how you feel it
is running now The file certainly Hey guys
you TK Speed is OK excreted Okay Anytime
you need me to control let me know
concepts Children It would take time to absorb
It's not something that will come in a
little bit of practice is needed unlike some
of your heavy subjects like deep learning and
all its Not that heavy because you are
already using readymade things like naive based on
all virtuality Understand It's more about applying it
in Fightin died in the challenge here The
challenge here is not so much in terms
off you know like guys too much off
heavy weight lifting in the brain They're very
very simple concepts you can't do complex concept
because we have not built it inside those
libraries So simple concept But the fight any
of the main challenge Okay so now lording
I'm just reading for every Okay then let's
do a quick recap Because what is downloading
We can't do much Really For the quick
recap of the class was Where did we
start the class today And I'm not doing
that He got You are doing it for
me What is the first question I asked
in the class today What is stacks Second
question What is that mortar Then what did
you learn in the class today So two
things to learn The stats and Morlin Where
did you learn what I NLP types What
else did you learn today Statistical and different
Statistical and mathematical Yes What as you to
learn how to compute the mean and variance
of 12 and three right Yes I know
Yes So I mean that I always check
with that Sometimes I feel I have already
talked to you and then I don't know
if it or you're not the next thing
What is What is it I would Did
you learn today In that last you learned
about NLP you learned about two kinds of
Penelope statistical in l PN sequence chillin in
sequential and you go with record and neutral
networks and all of those things Then you
learned about sentiment analysis parts off speech tagging
You saw parts of speech getting tired now
on and our objective and so on Sentiment
analysis I did it on the board for
you Then you learned within that double polarity
and subjectivity the polarities plus one people are
positive polarities negative on people are not positive
The subject levity varies between zero and one
It is not about factual day but it
is about subjectivity Then after that you went
from there to understanding how to make tokens
So you took the sentences and chopped it
into into sentences or on foreign two words
Then from there what else did you learn
You learned how to make it stem and
stem a stem and limb ization You learned
that then from limitation And stemming you came
up to learning about how to make it
singular how to make it plural I should
do some basic stuff about spell check and
stuff like that How to make this paychecks
work etcetera What As you do learn you
learned that there are three different types off
library functions out there The three functions are
I know DK spacey and explored So I
was using text block today The whole day
in the class Also parts of an lt
cable You would recruit him if you see
the library functions even see Analytica has been
downloaded Certain parts of an antique A after
that What did you learn You learned a
little bit about based Terram You learned a
little bit about naive Bayes totem I have
not really deep down into it I will
do that Reach deep down into it Five
You just learned at a superficial level Rejigging
your memory What you learn in the class
you learned a concept called bag of words
which you have not yet seen You see
after the claws but backwards and nothing but
a bag which had only got words in
it The meaning and the sentence And the
grandmother is lost Another important thing you learned
was that But these are machines They're not
perfect So you saw one example that we
were trying to pick up announce It also
ended up picking up verbs Right now we
are machines that are not perfect The key
important thing I told you in the class
waas that you can use some of this
information toe supplement your machine learning projects You
can say that You know Tim and LP
saying that customer data I saying that it
is negative customers don't want to buy But
And I repeat also telling you were customers
don't want to buy They don't like to
buy Sanders They don't like to buy shoes
They don't like to buy shirts Those things
you can use to do it My name
is on it Anouska Julia on Um I'm
walking into an addicts from last eight years
so I have done my injuring from an
addition *** on I have done my business
analytics studies from my sweet Okay today's topic
that we're going to talk about is essential
models so added and a list iam are
the sequential models actually that we're going to
talk about But before that I have introduced
myself but I want your introduction as well
You're not many So you can do it
cutely But with introduction I want something from
your said when accepting That is like what
is the inspiration behind doing this course on
Like you already have covered 60 to 70%
of the course So I want to understand
what you think about that inspiration right now
so you can take two minutes And I
did don't so that everybody has uh that
information return on can present or cancer within
time You you don't have to take much
time for that then so we can start
with Africom Anderson Okay You okay So we
can start from her from you You can
also tell about your work experience what you
are doing currently so that I get the
feeling what batches actually doing right now You
know you have to be a little louder
because everybody wants to listen to you Okay
But that's like you You know like you
want to do something in this field But
what it is actually because there are so
many things that you can do in this
real from where it started actually where you
come to know Okay I want to do
this No Okay then one more thing that
I know You guys want to switch also
and earn more money also And that's also
one of the reason that you trained the
score So it's very always to me if
you want to tell that it's also find
coming it's not a problem But other than
that I want to know more what you
think about the artificial intelligence and machine learning
So that's the reason Emma just asking this
question and accordingly I then talk about things
that we want to talk about today So
yeah you are You are a mid think
So I can remember these names actually because
you are like just seven people So this
there is a scientist Okay Okay Again 11
I I'll try I'll train my model that
way It's very good reason actually because I
talked with most of the guys actually who
are in high position on most ordains they
are not able to understand what I'm saying
Actually then first I have to take them
to the machine learning and artificial intelligence part
and after that I can explain them So
it told him is I think with uh
huh people on my side actually higher position
side that they want to learn They want
to like know what this deep learning artificial
intelligence machine learning is on Then after that
they can actually take the decisions that are
good for their company So I made through
it so you can talk about artificial intelligence
and machine learning with your colleagues on that
kind of self with your higher management so
that that's also one of the important point
Because if you know something you can talk
about that thing with your higher management as
well So it's just not the vote Yeah
of course it's important because you are You
join this course and you want to learn
all these things But if you don't know
the concepts you can talk about all those
conceptual Geo higher management you can You will
not be able to explain those things Doing
these practical things at your experience level after
10 years it's your choice If you want
to do it you can do it If
you don't want to do it You you
don't have to You can goto management and
you can say OK I can lead some
team I haven't always off this all these
things Yeah that's a good point You know
once you have that kind of understanding on
do you know the floor what things you
want to learn You can explore the sources
like you can go to Google and see
other documents and just 30 toe and then
hold to implement things you can goto But
you should be aware that the seconds off
all those things how from where to start
and where toe where you want to An
end actually So that's important Him and threat
They've Raj him and one thing more that
you talked about this rule based systems So
I guess everybody know what it is Actually
So he felt conditions that we're using this
always rule based So till now we were
like no till now but it's just 10
years back We were using that rule based
systems It fells conditions if we want to
make Ah if we want our machines take
some decision we just right if else conditions
And then they take the those decisions sexually
But now the situation is different We are
just developing some kind of a mind or
brain for those machines and they are taking
decision according to the data that's coming And
actually so that's what we are going to
achieve on deep learning or the topics that
we have to discuss today It's like talks
about all those things Also you're so okay
so a budget you I believe Okay I
got the names I'm it for it It'll
hit Yeah they run him and appreciate Siddharth
average it on bleep right So I trained
by models but with good So let's start
with the topics Today's topic So it's a
natural language processing using arginine and l scare
a list Diem's so first off all Oh
you already have gone to an LP Thank
you already covered this topic and help It'll
pick You are really covered Text genetics So
anybody wants toe Tell me what NLP What's
Texan Ethics On what All things you do
to do it expand Exe or you want
me to the couple those things for you
Go Okay You want me to do that
Are you want toe okay Sure Sure sure
Yeah So first thing Uh really talk about
text genetics Okay On in text and exports
all things you use how you prepare the
model X and X model What All things
are there actually So I have attacks now
I want to do it Eccentrics What is
the first thing that you want to do
on that takes Exactly So three processing All
right so in pre processing you what you
want to do storm 41 then duplicates You
want to say and duplicate it's OK We
can consider that also but it's it's so
is there special Get that straight Take stoop
Okay Yeah Yeah That's one thing You are
missing Two important conceptual That's like uh that's
not a part of pre process Yeah it's
a part of a pre processing but I
guess it's featured engineer not pre processing No
Yes Stemming and limited edition So stemming on
limit division So you know what these things
are Timing and limitation So once you have
done all this you have converted it to
Lake Lower case Also we can say I've
been Richard to lower case on If you
want to remove numbers you can remove some
cases It makes sense to keep numbers also
so you always don't remove no numbers So
after that what we do This is the
first step that be processing Next thing in
addition but I just wanted name for like
what That phase is actually future ex section
or featured engine And you can say that
Also extraction So were my words actually are
not good in women words So whatever I
hate this What I'm saying The focus on
that Not what I'm writing here So feature
extraction What Thanks to you do here what
kind of techniques you have for future extraction
for text Yes Be afraid of Yeah uh
bag afford somebody Yeah and then goingto boards
Andi When Northern Right You remember something Jagoff
n grams Magal Fangled Yeah And comes So
what we achieve here Well when we say
feature extraction but we are doing here actually
I'm just re capping If you think it's
like you already know all these things and
I can skip it also So this is
okay Okay Good So what future exception is
actually what we actually doing here So one
thing here if you want to train a
model can you give next as input to
that particular model itself That algorithm I'm No
I don't want to do this feature extraction
And I think just text I can't do
that Right I got So we have like
techniques These future exception TF idea of back
of words Count of words than bag off
N grams So you are aware off all
these things what all these are and how
we actually do things how we can work
it Name brick form using these techniques Got
a benefit or I want to go home
It's fine right So if I have it
takes okay Some techs like my name it's
under Okay this is the decks And then
I want to convert it to a vector
OK Are in numeric form how I can
do this It's important because of them This
is all related so it's very important to
understand this first on After that we can
sit toe this one So that's the reason
I just want to But he kept all
this concept first Yeah that's fine And we
have done the people assisting No the pre
processing is done Now We want toe play
Future extraction Yeah Okay I have ah two
statements and these are two documents D one
and D two Okay so the there to
things like under which is common in both
the things So dictionary will have only just
one knowledge for you Right So So how
the data will look like I want to
show that to you actually So first thing
it will be sorted actually So first thing
started I'm just reading a note will be
there the first feature first feature manage And
then it's break first break first and I'm
just yeah words Or you can say these
are the futures Actually these are the features
on then Ah is okay Whatever We can
just mention it Something thes air the features
and then haggle so somewhere Okay these are
the features Okay On Ben Now we are
If we are using bag of words then
what should be the value because these are
the column names Actually you can say that
we also then if we are using bag
affords then what should be the numeric values
Okay for even Andi to one Yeah that's
a zero for the run One 10 So
this way this is here This is victory
presentation on this is bag of words and
now you want to do back off any
drums It's very similar Instead off just one
word we'll have like n grams If it's
my grams then we'll consider two words together
If its Tri Grams then we'll consider Tree
was together and similar So bagel friend Graham
sister than count of words It's a similar
actually that same thing on Dante Afraid if
anybody wants to explain that the FAA gifting
how we calculate that So yeah in that
particular document in that particular document Okay on
what A vote in west document frequency So
I don't remember The form will just intuitively
if you see that if a word is
occuring like what be if I d if
does actually on why It's considered the better
future exception approach than these techniques So we'll
talk about that and we'll talk about into
intuitively Not the formula is OK So first
thing like everybody's a crank like in my
one document but 300 times okay on debt
Same word is a green lake I have
total number of the commence around 500 Okay
Andi that word is a Ringley Only one
day only in one document actually So inverse
argument frequency will be 500 We want inverse
One by 500 is document frequency and 500
way one is inverse document frequency Right on
If you well to play these two things
Okay this is like a number off 300
And And in this particular document I have
like around 1000 words actually So it's like
just see t 19 to 500 But this
number can be like hair in there so
don't go with the number but it just
the concept is like no probability or the
distribution What we that the f i d
E f number is going to be greater
But in case that number or that word
is occuring in all the documents on this
500 in that case you know mobility only
so it means utf idea of value is
decreasing so it has less importance But the
word that is a very really in all
the document has very high importance So that's
very intuitively that I explain it to you
without any formula So you understand there No
Uh so this is Theophile ADF And once
you have this back to representation you and
put this to your models and what all
models you have used for NLP or Text
analytics beginning Okay On Davis Norris Okay so
this is this is the thing that I
won't took over because this is very basic
for text genetics and be so that's it
from the recap position Now we come here
for do decision So So what are going
to be over agenda for today Okay so
we'll talk about sick until later on Then
we'll go over traditional ml for sequential later
So that's where I want to talk about
all these things Actually not I want to
use all these things here actually in traditional
animal for second Children on Then type off
analysts possible on second later using recurrence and
then Ardan and recommend neural network will talk
about why we required these A tenant actually
So Oh in between I just want to
add one more thing before other than I
also want to wreak of deep learning basics
because they are very connected to each other
And if you are not able to get
basics right you will not be able to
understand Aryan So I'll just explain it like
briefly here I explained NLP Same I'll explain
before the ordinance but first of all will
discuss all those second Chile time traditional ml
methods And after that if time permits today
will do the practical if time permits permits
If not then we'll do it tomorrow and
after tomorrow Okay for both decisions But today
our main focus area will be these things
actually So what you mean by sequential later
Anybody anybody want so uncertain Sequential later there
Time series is a sequence actually you know
you kind of know this time actually what
happened happened going down into the name That's
sequential later So yeah he's also right You
are also right Anybody else Okay so yeah
That's uh the sequence is important So if
I talk about text because the slide or
the topic that we want to cover is
natural and good Malinga's processing with arginine and
a less diems Okay so if I talk
about text data in a text this is
important If I jumble the words then it
doesn't make sense right It doesn't make sense
on if it's difficult for human brand to
understand this jamboree text Then you can think
about the machine because brain is more complex
than a machine actually So if brain can't
understand the dumbbell worlds Holy Machine can understand
And here in sequential models are really talk
about skins data on sequential data first and
then we'll talk about second tier models Why
I'm talking about the second sting hair actually
So you understand what second Children that is
Sometimes it is like he said that if
stock prices are like dude is like something
um some stock like 500 um they before
today's like oh 400 this all are dependent
to each other You can't alter all of
these things This kins also my toes because
if you not consider this on do you
try to predict tomorrow's data Then you won't
be ableto you have to consider this So
if you want to prepare a sequential model
you have to consider the previous instance for
their particular variable Okay so same with Tex
Tex you guard If I have returned something
in English then the text should be in
a sequence If I want to prepare a
second your model So what Consumer I have
to talk about that right now actually because
Suq intial motor is like something I want
to predict you have You're using words opened
when you write some statement The list of
words comes according to your words up that
these can be your next word So we're
predicting the next word Yeah so that's a
sequence you can't expect Like after my name
after my 100 Welcome It can't be possible
because it sickens how we write English Actually
so machine has to understand how we write
English It can be something like Okay we
can just build it up and then machine
will understand We should will make a sense
off out off it It's not possible so
that signature data and that's sequential model Also
they if you want to predict something that
they sequentially little or later to the past
in sense off that particular thing then it's
a sequential motor So you're predicting something on
based on your past It up Okay so
same same thing we have explained here so
it can be one dimensional Discreet index 10
instances correct a position that's important character position
here in case off NLP So point can
be skill A backdoor symbol for a Neil
for bed The one more example that I
want to give you Like if you talk
about this student who wants to learn the
transition But before that he should learn algebra
with the dirt he got Learn the transition
So that's a sickens right So he has
to learn that algebra first And after that
only he can learn the physician So this
is second child Later example ofs a conciliator
speech Okay I want to translate English to
German so that there is a sequence my
model has to understand for us But I'm
saying in English actually and after that only
he can translate It can translate it to
German so their skins makers If I jumbled
up the words initial words that I'm typing
in English then my machine will not able
to understand what all things I'm talking about
Source speeches one off the use case for
second shelf models Because this is also important
This is going to be one of the
factor One of the factor that there is
going to be like a lot off defectors
But this is the previous day Yeah previous
day closing is going to be one of
the perimeter for that prediction Yeah because the
two days or the next days you know
this price will be dependent on this The
last surprise So there is a relation with
Pinto Get on Still unstructured Yeah I talked
about like video video video video It's a
sequential later your audios on your songs These
are like sequential They don't you can't listen
toe one word Let the last five Brown
30 and 32nd first and then first or
second last Ugandan Willard So music is there
than protein The Mexicans stock prices in other
time Siri's OK so these are the examples
of sequential data So let's talk about lingers
Morning Okay so is the task of predicting
Woodward's come next So I give you the
example like you usedto chat onwards up and
then you get that the next word What
What It's going to be so here saying
similar thing We will just try to see
how it actually looks like what goes behind
it Actually How we prepared that kind of
a model Yeah er recommendation systems can be
off different things actually because here lingers from
Linganore Ling You have to have a skin
show Little for recommendation model That's not the
case Okay so the task is the students
open their and therefore options books laptops exams
and minds Okay so this this can be
Venable You're typing this on You'll see these
four option you'll get okay whatever you want
Oh so how you decide which one you
want to type or or your machine will
predict Which one you want to tape in
so foot back But we given a sequence
of words compute the probability distribution of the
next four How you do that You know
that how you compute the probability distribution are
like using and ground You know that Craig
you have the concept off end guns here
So if I'm talking about and gum here
we can see if we can go for
like t n grams or toe What you
want to do don't to So but you
will not make sense because there will not
make a sense Students opened there That will
make a sense So we have to go
for four guns Actually yeah Students is important
thing because without certainly will not come to
know which one it should be There actually
yeah Syrians opened there Then we have to
calculate the probability for that particular So how
you do that you have a became lady
Because really it's just a dictionary off all
the world's all the unique words But you
have on Then you really count the poverty
for that strange Open their books Property for
that given you have student open there So
I just want oh you know conditional poverty
You know the great listen given event given
event Yeah So I have like the students
open I want o calculate this probably open
There are I want to go with books
given given the students open there So I
want to calculate this priority So how I
can do this So here their books on
then given the student open there So that's
a statement We have given this thing right
We already know this thing on We want
to calculate the property off this VOCs fund
only I don't want toe calculate laptops exam
lines right now but books So how you
can do that So first you need a
corpus off text actually like you can get
the Shakespeare corpus or some newspaper corpus Okay
large amount off next eight Actually on Then
you will try to find this actually this
n grams number of times this statement occurred
in that particular corpus Okay So like let's
say I got the 300 times okay on
this This thing this thing how many games
it can be told in or something like
that So that's a number off So that's
that's kind of a probably distribution It's just
I'm not using the formula but you can
see this is the probability that I'm going
to get Okay So this is how you
can collect books so similar way I have
to calculate for laptops exams and mind richer
whichever off these words have higher property will
be Yeah we'll be there in that And
then fill in the blanks Yeah Yeah Students
open their on def books Has the higher
Probably Then books will be there Okay No
no no Corpus is something that you have
to pick Corpus is something like you want
to train your model Okay Uh like this
Yeah that's fine Yeah I can give you
example You're typing something in words that you're
typing Good morning Daily basis actually So you
are adding Good morning they Leavis is on
your Carper sexually so it depends on like
what's up What words up is doing So
it's it's for you only It's calculating the
Garbus what kind of words you are using
So if you type something in somebody's else
Movil onwards up it can be different The
number of physicians that you're getting or don't
actually world solution that you are getting It
can be different Okay Any problem here You
understood the concept How we are going to
get this Lange's mortar So this is an
LP Actually this is an LP untraditional ml
also and similar way We have toe developed
something in neutral network It has some problems
actually Yeah but from so property Is this
so you know the formal off I haven't
used the form will Actually But you know
the formula right Condition of already It's like
if I'm calculating for be off given be
then it's like intersection of B and B
and then B people already a p B
That's the formula And if you calculated it's
it's something Looks like similar kind of thing
Okay so what I'm doing here The student
opened there Book I want to know the
probably off this Okay given I already have
the students open their I already have this
Right So what I'm doing first I'm calculating
the number off names This whole this Let's
say we can remove this one also Actually
they doesn't make sense Way can just have
students open their books So students open their
books we can count number of times This
actually happening in the corpus corpus is the
text that you are selecting You want to
get it from Internet from some newspaper site
or from Shakespeare's nobles It's on you actually
So how many times this information or this
text is happening together That's on Engram actually
together in your corpus Okay Once you have
this you can You can also go on
the number off times students open their happening
together You can also go in that right
So if you just I have just taken
example 300 games happening this on 1000 times
that's happening This this is the probably that
you are getting So that's how you can
believe the probably So which word among these
four have higher property will be assigned or
will be faded Help on this Okay so
clear Right So here you can see the
example So you do these kind of things
You know all this Hey also So they
are using some lingers more like Nordling is
in order not lingers mortal They're using Lange's
mortal but nor traditional animal models Okay Lange's
mortal can be in traditional M l metals
and can be in deep learning So deep
learning way are going to talk about our
and and and Alice teams So they are
going They're using those So how will I
already explained it to you Now there is
a question How to learn Elling is moral
Learn a and gone lingers mortar So that's
what we did here right We learned that
and wrong First in gum is a chunk
off and consider Divert It can be uni
gram by gram tie grams or 4 g
But we have used here four guns right
click statistics about how african different and grams
are and used these to predict next word
the same thing we did here Right Good
So here the mathematical part are just explained
in this mathematical occassions but nothing to worry
It's a similar thing here You can see
first we make it simply find assumption That
depends only on proceeding and minus one so
and minus one depends It means like here
I choose this Syrians open there These are
the words the next world depends on before
this I don't have any dependency actually on
my and forget the world So help over
the day off and gum we calculated Okay
how you calculated and then probably off m
minus one Come We did similarly her like
this is an minus one right This is
an minus one And this is and actually
on How do we get these Engram And
probably is by counting them in some large
God puts off next That what I told
you right So it's clear now So here
also statistically this this is approximation off it
Actually if you see here this is approximation
We are using the probabilities but we are
approximating it too Go on because it's very
similar The probabilities are the numbers that you
get from here It's going to be very
similar If it's a large text off corpus
God puts off extraction No they have before
it got 1st 1st After that whenever you
start typing in us time you started using
it Then whatever word you are using are
also getting in orderto that corpus Yeah So
that's the reason you're what's up Use so
much memory Yours in that space So everyone
is clear about this concept Great Yeah it's
the same formula Whatever I have mentioned here
it's written in mathematical equation So if anybody
has difficulty in understanding that can remember this
also there's nobody will going to ask like
dedications Nobody going toe Okay so I have
taken X t right on what I'm saying
I have to Oh I have a dependency
on and minus one words Okay And d
plus one words I have to predict 40
plus ones Word I have to predict t
plus one word So now I have to
move And minus two back Right So B
plus one minus on minus one actually Okay
Demons So it's t minus and minus one
plus two on minus one words So it
is just that my maternal television eyes Okay
What can be do you bless one on
Tu minus plus So you can the first
thing that first formula that you're saying the
prosecution be X off T plus one and
then x'd extends to x one So it's
considering all the all the words that are
there in that first thing right on then
in second thing But we're saying that we
will not look for all the previous words
We just look for the words from like
we're starting from this position Okay If we
want to predict this one we'll just look
back these these three numbers So it should
be the XLT if I take this example
and I want to write that thing So
how well rate it So it will be
like P off X I want to predict
this is like which would fourth 4 ft
It's the fourth night And given what is
its going to with It's young That's so
this is going toe me like x four
I want to predict export I want to
predict and I'm using x three x two
and x one So just a mathematical occasion
Algebra thing Simple Okay so that you won't
go have before going off before predicting it
19 years next Yeah that's the same thing
right Suppose we're learning a foregone model Okay
Opened Yeah No I have a piece that
that could have ever Schools want their pictures
more list seats But then with the more
product here No So you have to carefully
select Yeah that's the problem We'll talk about
Actually there are problems And we have We
will talk about all those problems with their
traditional animal actually So that's the reason we
move toe neutral And 12 more than that
That's on you Yeah that's on you But
actually we will decide that you want to
use and this uni gram by gram on
dry ground in them through You need them
like that You have only just you have
to check the accuracy That's how you can
decide which uni gram you have Teoh this
which and gum you have to use You
have to drive with multiple things I No
whatever you are mentioning if you want to
just mention like for grammar trigger because you
have to calculate these old things you have
to complete before But that's the reason I
took that to recap off externalities who goes
in that we have to prepare that vector
first So if that is part of feature
extraction if you don't do that then you
will not be able to train That's yeah
that's what I'm saying So first thing But
when you are developing a model okay what
are things you have to do in text
Text analytics First it's pretty processing then is
feature extraction in feature extraction What you will
do if you are using and Ingram's You
have to specify which and Graham you want
to use Okay it's going to before Graham
are Try grammar by Grandma's unit Graham You
have to do at that point of time
when you are accepting the features Yeah so
that's the reason Mortal bill that good it
will not understand anything You are just providing
the nomadic or no medical data so that
for the that is that you're yeah exactly
Exactly It's so this angry You can keep
it No no See there are problems and
we'll talk about all those brother and that's
one of the problem But theoretically you can
keep it anything whatever you want particularly And
then we will face some challenges with infrastructure
how we want to keep that kind of
for data with us So there are some
things that we have to talk about actually
on what our challenges are there in traditional
Ml and we'll talk about OK so you
understood all these things So here he we
have explained it and like giving some examples
You want me to goto this example or
that's on the example that I have taken
It's good enough Everyone claim we can No
heard Okay so now we'll talk about problems
with handgun Okay So first one the one
he pointed out actually that we don't have
that occurrence in that court press Actually whatever
corpus we have selected now I want o
calculate students open their books Let's say the
value is books Okay W J that you
can see it's books Okay so I want
to calculate that probability on but this stored
in a store in tow open their books
It's not occurring in my car Personal together
What you will do then In that case
so uh huh Random assignment for that particular
thanks Okay but when once you model is
in like production on you in going toe
Sorry You encountered something that sort off Okay
so in production what your mother will dough
Then in that case yeah it will get
failed on it will not I will go
show what's going to be the next word
actually So because in that case your first
count will be zero Yeah I mean your
accuracy is it's not there now because you're
using it for some purpose And that purpose
is not Mac actually So its failure off
your more elected so first thing that counts
student open their books will be zero Because
that Texas not exist in your corpus So
in that case how you can solve that
problem They they have given something the partial
solution that you can see x more limited
auto going for every w J But this
is one of the thing that you can
do Okay Apart from this there are other
ways Also it's called LaPlace moving the one
that hair shone It's called hapless smoothing And
what we do here we'll just add like
in count If it's zero we are just
adding a limiter Lambda there on in at
the bottom going through and open their plus
number of words in became Larry Okay we
are heading back Okay It solves that problem
but it will not predict correctly every little
that it's not zero Actually you're probably It
is North zero but it will not give
you a prediction Random number Yeah exactly On
the 1st 1st thing when we got this
problem but there I'm done Actually they I
did One plus one north limit on what
Plus one last one divided by this I
can give you formula Huh Uh so that's
going last one on then Going off the
bust Spence That's less V So they have
done this thing for us Actually they have
our other plus one But todo problem you
are still giving it one occurrence and your
corpus 11 divided by the count of Yeah
Yeah so they want to decrease that probability
for them Or so they said OK instruct
off One go for limited Lambda is very
small number So that's a cold hapless That's
morning executive That's it That's it Nothing else
we are doing here It actually says that
you have calculated the probability off the next
word whatever you are predicting But actually that
whole sentence doesn't exist in your car purse
But you are saying there is some probability
that this can happen It's greatest So I'm
saying like books W is books on it
doesn't occur in whole corpus Okay so what
I'm saying I'm calculating probably of next four
It can be book What is the probability
that the next world can be books Okay
given whatever the previous statement is actually whole
statement So here if that particular not books
What actually poverty off w students opened a
book So here don't student open there book
the counterfeit So there is no count actually
zero cone on Then you're riding some small
value to it And then after that just
waiting the count students open their if it
exists in that corpus on Do you have
some value here But what if if that
students open their also doesn't exists in your
car pass And that's a problem because you
are the wedding play zero That's something that
nor the probability So that's the other problem
Okay so ho you do that But this
is this is some kind of example Okay
so this this same example But you are
like making sure that students open there is
like a statement And after that there should
exist something But it can be that we
are just end three words off a statement
And after that there is there gets nothing
but yeah but you think that with traditional
ml techniques you can do that kind of
stuff Probability is not zero going t zero
Yeah I mean if you want Yeah Yeah
probably the zero But you're talking about the
concentrate If it doesn't exist that so and
so open their books doesn't exist Then it's
zero count Is it One of So how
you prepare your traditional animal models for text
analytics You do pre processing You do feature
extraction And then after that in future exception
what you're doing you are preparing a numeric
form off your converting text to numeric form
and then you are importing that numeric toe
your moral But if you want to do
this I'll do that You have to take
care at valuer converting it to numeric form
Yeah that's the reason traditional ML doesn't work
for a second Straight up Yeah it's it's
on you Actually this is just example Okay
So if you feel like if I just
buy grounds and it will give me better
than you you can go for my ground
actually But sometimes we will have to hear
because if we are choosing like we want
to go with 4 g like we want
to know the fourth word OK then we
have to have a try Graham Also because
that's what will be like used for as
like to predict the Fort Worth So we
want to have they come and 4 g
also But that's two vectors only That's don't
only input betting that we can use And
that's during feature extraction Only they're only you're
manipulating all these things Yeah you can't give
something like two inputs to your model Uganda
You have to do it in future ex
section And after that whatever you probably you're
getting you can give it to your model
as input Yeah Okay so the okay We
didn't talk about the second problem right Yeah
So But if students open their it doesn't
exist in your data then what Then it's
a bigger problem right It's infinity Probably gonna
be infinite So there just condition and open
their instrument This is a cold Becker So
instead off going with like syringe open there
we can use open there just open there
But here we have to make over like
this feature extraction this way How we can
add that how we can or we have
to write a logic Okay If we are
getting the probably zero then go for like
2 g but with like just last words
that kind of logic You can also write
the first one also But how many games
If you are dealing with foreground of 5
g then Yeah it's like you're using machine
learning but still you're using a rule based
thing They're So you are not doing something
that is very just useful So that's two
problems And then there is one more so
What's that need to store Come for all
possible And gum So mortal sizes Ah you
know the complexity Thank complexity of modern whatever
model you have study or feature extraction technique
PC if you know you have principal company
delancy Yeah you're lived in their correct So
you know the complexity level for peace here
that kind of stuff right there So similar
way It's also a feature extraction techniques So
here the complexities explanation off an on his
number off words that do you have in
your become so to store that kind off
a tank I took a lot of sense
If your car passes you and for actually
for like prediction you need that large amount
of corpus So that's that's 30 problems that
you face in traditional family You can have
practically you can have particularly you can have
But if you want to implement it on
like tactical basis it's impossible It will take
to train your model It'll take like years
Yeah so recalled on Lange's mortal task What
Skins Awards for the distribution of next word
That's what we have done Well now right
whole about a window based neural model So
the window We are already where Window is
Nothing but what's the seconds What's lost the
forwards on If we say like forwards we
have to consider than Windows forwards Okay so
we want tohave even know based neural model
So how we can achieve that So you
already started neural networks right So you know
the basics Anybody can tell me that Uh
okay I will not talk about window based
neural model first but we'll talk about neural
networks Okay so here you can see the
fixed window The students open there So that's
a window here So we are going to
use similar concept in neutral networks But it's
coiled fixed window not N grams are kind
of stuff So it's slake We have time
You will take a break on 11 11
30 11 30 year We have time No
problem So anybody I can explain it to
me This thing Have you seen this kind
of thing And your neural net approx sessions
Hair are for nerds actually ever four nodes
This is just one note on Then we
are getting output I'm not explain a lot
of things here on this leg because it
just that and to give you information about
what neural network is actually and how we
can use that way have just have it
here But we'll talk about the noodle network
first the basics We want to recap all
those basics because it will make it possible
for you to understand it more clearly Ordinance
on Alstom's So we talk about all those
things So here the thing that I want
to talk about this again you can see
that one Heart victors Onda we have concocted
later on word on Then we have a
day earlier and then we have assault Max
What We go talk Max winds And what's
this actually off this layer This soft max
function that we have used What It is
actually called in journalists Yeah its activation on
what is this he didn't live on And
General Lee's name for it Aggregation function We
use aggregation function right They lose their Yeah
but most of the times we'll talk about
all the I'll recap All those things for
you actually because it will give you a
heads up What we are going to cover
and for an organ and on how we
can relate all those things OK but what
the word soft makes I want to ask
a word Soft makes uh anybody veered off
soft makes function Yeah It gives you the
distribution property distribution but someone for one actually
So Viagra Right So here the similar same
problem statement on we got probably the situation
for books The highest Actually that's the highest
And then after that laptops Okay so the
output will be decided by us sexually Which
one I have to pick inwards up You
only see 3 to 4 words in your
position box Not more than that So what
All words will come the all the words
which have actually higher property will come there
and your solution walks Books will be their
laptops And then do three more words Whatever
Having hired Probably So let's move it and
will be capped So here you can see
improvement over in gum here knows first the
problem That thing we'll talk about hope But
right now just what all problems are what
all improvements we are having here with neural
network We will not have that common physical
zero problem here OK we tried toe We'll
try toe learn the language is actually the
noodle electro Try to learn the language itself
on then mortal size or the complexity will
decrease Actually OK so you don't need that
kind of infrastructure that you required for like
um your traditional and Mel mentors remaining problem
fixed window It's going to be still there
Okay not enlarging window enologist and w So
this is like these are worth w and
you anybody there what we call it which
Yeah it's rage So it increases that never
be large enough that already mentioned we don't
share way It's a course of Indo So
we'll talk about this last point with our
dinner because that's the That's some property off
Ardan and actually uses different rules of the
little We don't share rates across the window
so no will solve it the recap Okay
so you have deep learning right You already
started a neural network on these are the
areas where you are Actually people are using
on deep learning in Dennard and clothes Miss
Classification So in CNN what practical you have
done what you have done I m is
Really Yeah So you you do this means
classification as a problem statement are Okay So
you are aware of CNN how it works
You are Ah where are fooling padding You
are aware of pulling back there tonight Okay
Great Lancaster translation is there in Lange's translation
It's with CNN It's more possible So with
Adan in its possible okay then lingers processing
it's again without and given analysis again with
the other known recommendation ordinance again Can Cissel
detection grading drug discovery these most of the
things we are using our in and actually
CNN is a misclassification mutually Where we have
input as image because you know that gin
Yeah yes but for sentiment analysis you have
a text Okay on Do you want to
understand Okay If it's written in English you
want to understand for whom they have returned
or for whom they have shown that kind
of feeling Okay to understand that you need
that kind of a second chill model or
lingers more intellectually which can understand your previous
words Okay so that's where like this Ardan
and recurrent Newton light welcomes in picture So
media intervention entertainment video captioning video search realtime
translation It's all happening through this deep learning
stuff on using arginine Actually not Ardan and
Ardan has from the A list IMS That's
long short term memory So you remember Ghazni
Yeah so that's kind of against that they
are using Okay then Security and defence face
detection Face detection You have to use CNN
order Not most machines by the Stadion detection
lane trekking recognized her fix signals So these
all are like hold it in place using
new network these old things So Ah I
guess in India you haven't seen this these
things yet Face recognition Is there your phone
Your lapdogs Yeah Video captioning It's not something
that as a normal people we do right
So But it's there It's there in film
industry actually So if you talk about Phil
Oh yeah it's there in Amazon Prime They
know you're watching something and Amazon brain You'll
see the number of the members that are
present in that particular scene and their information
So they are you using doing that Video
captioning But for the members in there for
humans that are present in that particular Yeah
gaps Leg No Okay Okay I haven't Okay
Uh okay I haven't seen the idiot Then
I'll take there then So that's like the
areas where we're using deep learning Okay again
the use cases different use gets the same
claims on here The thing that is that
every time cities log analysis this protection So
risk detection is one off the areas that
eyes like Well we are using the area
we were using deep learning by because it's
no You think it's a sequential later or
there is a pattern in something you're doing
huh Risk this introduction if you're working for
a banking orphan and Children man on Yeah
people if I have a credit card Okay
I'll do particular kind of transactions only Okay
on if I go like on do some
by something the friend from my regular transaction
and do like 23 legs transaction It's not
my nature It's not in my nature that's
it Sickens right Yeah So that kind of
thing also possible Yeah They give you a
call and confirm OK whether you have done
this transaction or not So this is very
important actually because right now you are asking
in India it's not possible actually because whenever
there is a fraud on your credit card
if you call them and say OK this
is a Ford A long time ago I
guess on 5 to 10 years back they
will say OK what we can do Oh
yeah Way gonna do anything for you then
But outside whenever you report a fraud they'll
give you that money back That's their done
But then it gets very difficult So now
they're using these techniques But 10 years baggage
It was not there it was not there
But right now it's there on They are
also because they don't want toe be in
Los actually So if they have in Children
but still they are paying something for that
insurance Right Insurance is there but still they
want to save as much money as they
can So yeah no it's It's everywhere Every
were Everybody wants to be in profit And
that too What if they have a technology
They want to use that technology They don't
want todo go for insurance and do things
like that If it's like under percent foolproof
then they will really go for that Why
they will go for insurance and all That's
the challenge because it's very difficult Oh give
that kind off a justification to higher management
Okay if they are not into artificial intelligence
or machine learning if they haven't taken the
kind of course they should take Actually to
understand all these things that's difficult It's very
difficult toe Give them that kind of understanding
Okay what's happening there and how Nuland drugs
are working and all that kind of stuff
It's difficult but in future it will happen
for sure If you are getting that kind
of accuracy their artists that can have been
off the desert You see those other than
you can see that I couldn't see also
on def you're getting on the percent I
could see why I know what he wants
to move through it just that they need
to learn a few things more as a
higher management But then put that you're giving
that in controls right You are giving it
toe new electoral But yeah a young that's
fine But the input order features that have
that you have given to near limit network
that's decided for you You know what all
features are contributing to towards that particular time
on there will be very their case where
there is a mismatch and what the conclusion
is and what actual authorities So there you
can see you at all the parameters What
You have a pass for that particular transaction
you get the tanks It's not that it's
impossible but it's like a kind of a
tricky thing to do that it's in This
isn't p or in the forest You easily
get all those information going forward with this
Also real get something So what We are
going uh going to achieve using deep learning
We're trying to do what We're trying to
create a brain for a machine and you
think that right now we are in a
position toe we make the human brand even
new neurologist they are not into like they're
not able to understand this complex brain Even
they can't explain how brain function actually so
we are trying to learn Ah a small
small chunk off information Okay And then we're
tryingto see Okay how we can do how
we can use this information and prepare brain
for machine actually So human brain you know
list inside You want me to believe that
anything here the last one get ready to
remember and connect things That's ah thing That's
the friend with human brand with machine It's
not possible remember and connect things All right
I agree So this is artificial neuron were
trying to replicate human brain But this is
just wonder and you know the brain how
it works right So the other neurons and
then how the information forwards and the whole
this old triggers So this is a single
neuron so anybody can explain it to me
And it was like proposed in 1943 So
you can think about like it was there
in 1943 now we're talking loaded Why So
first tell me that prism anybody available that
you know Nice song One of the biggest
reason now competition And I don't think yeah
data There is one more thing that these
two things Actually because of these two things
we were No they will do achieve anything
using this on we were not able to
explain And nobody wants to actually listen to
all those schoolers Because previously PSD guys are
masters who have done masters in like besotted
chilling dozens used to work in this field
But nowadays that this is changing on now
people are walking on There is a reason
for that also Let's that treason Uh huh
Yeah the tools The tools are available Well
you it's being like 60 70% Of course
And have you return l an algorithm by
yourself Hole and go to them Have you
No that's that the problem You should You
should write that You should understand the maths
We handle it But to understand Matt it
takes a lot of time Actually on He
was right that he wants to do the
full time there You can actually do all
this that kind of a thing to understand
the mat and then try to right that
holds junk off gored by yourself not using
the fighting package or some libraries Okay but
that will give you much more sense than
just strictly using the package or library These
air aggregation function and these air activation function
right Rico will keep legal because this is
just a recap so aggregation function integration function
this responsible for aggregating input signals from other
neurons control boosting inhibiting or completely suppressing the
given input These things it can do actually
on then most common integration from Shoni estimated
applications implement doubling back to multiplication So this
is like wait on dure input I God
activation function still So first one step function
right You know that's a function if you
are f f is just input If F
is greater than zero then activation function will
give us one instead function on If it's
less than zero then it will give us
zero So this is a step that I'm
function You can see if F is greater
than one Then it's run If S F
is between zero and one including zero and
run then it's F So it's a kind
off ramp on benefit less than zero Then
it's deal so sick might function I guess
everybody knows about it So hotel training but
step thrown so anybody wants to expand that
that concept Hold it in that What's up
Going You already Okay Uh yes And it
Can you draw it here Because I just
want it in blocks on Lee I just
I don't want any back tres or something
Nothing Just the process How it all works
We say we say it's back propagation but
how That back propagation actually looks like the
flew off it Anybody aware off it hold
back propagation works the flow off it You
want to just just blocks Yeah Just try
it If if you can just start from
okay this is your noodle network Okay on
then how you do things There is the
import So you optimizing the weight but depending
on what learning rate is there but what
you're optimizing So what that other is that
lost function that's lost function on We are
actually minimizing the loss function So optimizing So
you have ah demolition as a subject in
the curriculum or no No Okay so every
machine learning problem statements statement is a optimization
problem Optimization means we want to reduce other
We won't do produce Adam Yeah So that's
what we want to achieve And in unsupervised
learning It's not the case because you know
and so always learning were not predicting anything
We are just segmenting something or questing something
and they're what's Ah lost function then just
in intuitively I don't want any formulas or
anything but it just what you want to
increase There are decrees there to get no
not number of Richard's in clustering or unsupervised
learning what you do Actually yeah exactly It's
distance You can say distance but difference between
two clusters You want entities that so it's
not You want to minimize but you want
to maximize their distance actually between two clusters
So that's that's also optimization problem Okay so
optimization problem cannot be machine learning problem But
machine learning problem is in optimization Always always
Okay Thank you So I want to explain
it actually in more detail So how actually
noodle network folks on Gwen We say like
back propagation What it actually means on how
we are doing So you when you're going
through this back propagation how you going through
it You started mathematical occasions O r intuitively
only But when somebody explained to you how
he explained through my medical a question or
intuitively they told you But so how you
calculate the minima lost function I want to
minimize my loss function Or you can calculate
the transition Yes partial differentiation Exactly So you
know defense Ishan You want to talk about
it mathematically or not Or intuitively we'll go
into duly Also we can go We can
go intuitively And after that we can come
to the physician because it's all important A
little bit important They didn't dissent If you
talk about it's very important to understand that
also right Because that's how we are actually
minimizing the loss function And then we are
achieving the part or you can say the
shape of the data Okay so first will
talk a vote okay Deserve my in pulled
neural networks I'm talking in blocks actually So
lost function So you gonna listen these things
right These are input X one x two
x tree These are input The love it
Okay Andi at the start off when you
submit your neural network when you start training
it So at the start what should be
orbit So how long how you can do
that It's always randomly or you can initialize
it to use It was also sometimes I
and I'm right So But there is a
problem on this There is problem always Actually
if you if you randomly located there are
chances that because you are selecting learning rate
also you are selecting you are randomly selecting
these There is a chance that you skip
your local minimal if you're learning rate is
too high or your rates are too high
So in that case you you will not
find their local minimal that you want to
find as a loss function You find the
minimum for that loss function right So that's
your whole purpose You find that minimum for
that loss function So that's the whole purpose
Often noodle network whatever you starting here that's
the whole purpose Scenarios can be the friend
It can be seen and it can be
undone in a less timis gr use But
the major the thing that you want to
achieve its two minimize that lost function and
to get that minimum loss function it depends
you know they're also you have you can
select random also No And there are different
ways to optimize that also because It's very
important step Because if if you you have
a data points okay okay on you want
to but like the Shape prophet Oh are
you want to model it on You want
to predict it You want to minimize this
last four or the others that you are
getting you want to minimize from where you
want to start It's very important if you
want to start youths start from the midway
only then you don't have to travel much
Your training time is very less in that
case But if you are like starting with
zero zero's then it's going to be like
take some time Actually it's always you have
to keep in mind You have to write
different combinations So that's the I guess the
challenge with new role and you have to
multiple things And after that only you will
be able to see Okay which one is
giving Which combination is giving me good accuracy
Actually minimum loss function Okay so what I
won't explain here This is your mutual network
It has like hidden layers in poorly are
player everything Once I got the old port
I have a lost function Okay lost function
can be Adamis You know that That would
mean scare So you have predictive predictive values
also and you have actual values Also you
are just calculating This group means scare other
on its in a function for might actually
So let's not go into mathematical equation First
real explain intuitively and then we'll go into
maturity allegation and once we have calculated it
Okay so we have to calculate using the
French mission for sure we how to calculate
minima the transition and is equal to zero
And then you'll get the point there It
will give you minimum Uh that that function
will give you minimum value Okay loss function
will be minimum So according to that whatever
we lose you're getting so I can explain
mathematically for with simple example Actually Exc your
school X Okay This is my function Okay
let's say this is a lost function Okay
on when I do the transition it's two
x plus two is equal to zero If
I have to calculate minimal I have to
do this on then minus two through this
minus one So X has value minus one
No my This lost function is vacation off
like weights Weights are there than learning radar
there on then input are there actually inputs
and then outputs off hidden layers Actually So
I will defend shirt it on bases off
my Bates because I want to change my
rate So whatever way it's whatever rates according
to this you'll get you back propagated to
her or to your hidden layers Also you
have to give it to all the hidden
layers proportionally whatever Like a very few got
Here you are getting toe whether one is
in this aid earlier and one is in
this year earlier than proportionally you have to
divide the way It's also so the friend
hidden layers Okay on Once the Brits are
initialized again the process will repeat herself until
the time the lost function is no changing
Actually you you have got the local minimum
Now the loss function will launch and stuff
the fix No it it will be there
for So that point of time you console
pure algorithm Your model will stop learning That's
it It learned So that's the basic intuitively
whole The new relent broke works but in
traditional And is it possible to do all
this I think Yeah exactly We don't do
this all this kind of stuff right So
this is you are using simple command and
beytin toe implement this like neural network But
behind that demand there is a loss function
There is back propagation There is hidden layers
Everything is there Yeah If you want to
check that code you can check that Good
Also goto get home for any particular library
Whatever you are using you can check back
and tryto understand what they are doing with
the court Actually this this This is optimized
Europe Amazing This lost function When you're doing
the transition it's opening position your deposition Europe
realizing it Too many minimal Anybody have any
doors can come to me We can talk
about Yeah interest off time Let other guys
join Real started So he also when we
talk about records so far we talked about
the feed forward networks on one variation of
the feed for a network We spend a
good amount of time What is that below
is CNN CNN is 100 She has very
good application in case off computer vision right
now If you understand this pregnant work again
Our standard practice So we need to understand
What is this even before What is this
Why we need that Okay In which situation
We can use that on what special award
That Then we'll go into So don't worry
about this complex equation You will easily get
that That's my duty within one over You
can get that wanted this Okay then we'll
practice one example also So let's understand Even
before going to this Ah decade self Okay
so I used to Tom's right One is
on a special related one where we can
use the CNN type other one is something
a temporal Let me start with simple examples
Okay To establish when we require this If
I talk about OK we used to play
the these Ah riddle straight If I say
1234 then question Mark What will be natural
answer after that It's okay on the way
that we can be tricky else Right 137
Question mark If it you need to identify
the pattern in that do some math and
it will not be visible Why did that
But it is in my application Are some
division There's That's a complex operation We're doing
it I'm some people Enjoy that Also those
reels OK but if I give only one
or two can you get that sequence patterns
difficult But if you have sufficient information then
depends on your I Q level Some may
be cracking it within a minute Some may
be cracking a 10 minute That is a
different story Okay so that means whatever in
case of images are in case of any
of the decisions We're not talking about the
importance of the sequence If I talk about
one image I I'm talking about classification of
that with the dog cat Or maybe what
is abnormal Normal type of It doesn't have
any dependency on the previous one but you
may say the same thing Can you see
Think off some situation Just like I'm talking
about number There is a sequence Okay Are
something like a weather forecasting Is there any
temporal sequence in that To predict the weather
all the time Cities Okay look we put
that with So before even doing this Ah
deep learning approach of the time series What
are the techniques you are familiar with If
I give you some time Series later Okay
So what of the techniques You are familiar
with any mom Very good We're talking something
else Exponential Smoothing Yes Okay some regression matter
Sorry My is a combination off toward aggression
and then moving average Okay expert you notice
some traditional techniques are already there but we're
talking about How do you Saul these approaches
okay With the deep learning that's what The
thing for us for the next two hours
for us So in this also whenever there's
discontent over time series data so does it
need to be always numbers I doctor about
sequence were able to predict it Now we're
talking about weather forecasting We're agreeing that there
some numbers and all but it doesn't need
to be all these numbers No Right Okay
What can you tell me The other example
Ah speech Very good speech You made this
also which is kind of a video frame
or this one and all There is a
sequence importance is there Okay then Ah music
speech A music is also another This even
we can produce automatic music Call Cherie now
comes to the text That's what will focus
on So text What kind of example You
can tell me Chad What is one good
example Okay but that is Maybe I'll take
that But after some time I'm talking about
Let's stop Because what needs to quite a
lot of knowledge Okay even billing a chart
But is also anyone good application Not Okay
So I'll talk about where the maturity and
all that that maybe tomorrow I'll doctor I
may not get time on that but in
the text also let's start with a simple
example Yes very good example Yes So the
world feeling you are talking about Yes there
I'll take the same example even in our
class now to establish the need for the
recurrent networks and that there are different types
of recommend What Maybe some of you heard
the name of this crazy When I heard
that first time I was crazy We're second
Would long shot though memory What is long
body shot Got it So I can understand
if some people who are hearing for the
first time looks crazy on that I told
you Are the commander do they say that
before even deep limited focus under the words
you'll get the clue on that Okay There
is also a lot of flu Well justify
that What is there in that Okay But
even before wearing that fast a few minutes
I'll talk about general different networks Then we'll
establish what is the need for the little
complex networks like this And there is something
algae are you and all gated recurring unit
and all but at least in the interest
of time will cover only one of them
That the last name is good enough much
popular And after that you were in the
game Tomorrow Should be what I'll throw you
some real world applications You should tell me
It's not like only either our type There
are applications were I use combination of LSD
on CNN Okay so that's keeping that end
goal Let's learn because it's CNN You have
some comfortable understanding No So the same level
of understanding Let's get from a less team
Then tomorrow we'll talk about real world applications
in which situations I need to get What
kind of applications will help me to understand
that Okay that's our end goal So if
you start with this So here Okay maybe
looking like as complex as this In our
globe network it looks like straightforward feed forward
past a perfectly OK so but this one
is not as simple as this Okay so
it will be looking very crazy like this
Okay connections backwards and all You have a
lot of applications You a little I need
not go again on that So let's understand
little bit on this further their first let's
focus on our own And then I'll goto
the I have in this picture even again
I'll talk about that complex What you are
talking about here Okay so this is more
like here You already How What for Anything
that somebody If I take a hidden stayed
here it let me take a different color
So what is input here And what is
output here for this particular North Input is
coming from the previous leader ongoing That's it
There's no recurrent connections What we're talking here
is if I have a connection back to
hell Okay So that means if you see
in a time cities manner because if you
relate that time since you can easily understand
it Okay So one day said just putting
the recurrent just like effect and related to
we were Ryker Shin How many people remember
the program That portion in one land were
doing it But if you want to explain
what is the question is more like what
Maybe for loop off 10 times it is
doing in that precaution Okay Are maybe multiplication
can be explained with the sequence of additions
The same simple methodology you can see here
where the recurrent means there are Sometimes it
is Okay how much it is to be
Rick how long it can be That's the
next question OK so the recurrent networks we
require the recurrence But how long Okay that's
what we'll see with examples But the simple
understandings will talk about this later Okay where
this will fail I'll come to that I
will establish my point in next maybe 10
15 minutes and then we'll go to the
complex things like that and they still will
ignore that So oh let me take it
because I want to spend I can take
any example off the weather forecasting or stock
on this one but I want to intentionally
take the text because we'll not get too
much time if I miss the text here
So sentiment analysis You guys know Okay What
do you mean by sentiment Analysis It is
a positive negative Wendell Okay Is there really
isn't like economic product or really that there
is any use case for the industry feedbacks
Exactly OK there so many people who do
the crawling off these websites from this one
that takes even decision in their long term
strategy Also Okay so that yeah financial Laurie
maybe Let's impact on that And even let's
say the vials are what needs to be
the next mobile washing because they're launching every
quarter every year every quarter memories Right So
they have There may be some road road
maps in terms of what What people are
praising about what people are creeping about off
a particular model so that the next portion
should be solving that otherwise they can't increase
the seas I will do that But they
have a what you say Ah reached a
level then it will be difficult to cross
that right The expectations were is also so
Apples apples How many creeping Say how many
gripping videos came you have seen in New
washing comes right Okay So because it was
a good hit in the beginning that know
what is happening The expectations Also we want
some that delta Okay the deltoid is not
able to So that's the reason I'm saying
my my view there Maybe it was the
business class people who've been ah so But
when the initial report comes or maybe initial
iPad comes and maybe initial iPhone comes at
this tremendous Okay that's a different but the
same level of tremendous difference We can't expect
it This dish Ah now it is a
game in terms off capturing the market and
ridges and they want to reduce that I
think that two days ago they are saying
they want to do the prices again in
India to capture the market or not So
they captured initially even because once they exhausted
in us what happened in us if I
remember still wants in San Francisco and I'm
traveling in the train Nobody looks from the
next faces And when it just had a
glance said that when I'm walking from one
compartment 98% of the people are talking taking
the iPhones with no other brand at all
OK but they will use even if I
see my clients who are coming from there
They still use iPhone four iPhone five Also
Okay So what happens They need to throw
that and then get a new one Right
So then robust is also so that when
they do Eric frequent really is that one
can't impact that market Okay it may impact
other markets OK they have That's a business
limit Let's come back toward the planning part
Okay So that there is a reason I'm
talking about is on There is a good
use case off this and it's not a
small problem but it's a very world problem
Okay now just like you are talking about
the Hindi English is the same thing is
more prominent for us to see the Indian
washing For this in the sense English it
may be a little bit sorry but when
you talk about the same thing for Indian
market because we need to worry about because
a billion mark billion dollars of market rate
So that's what people are talking about Indian
languages But it's a speech or text Now
I held this The sentences can you see
by just seeing it with the positive warning
I wish it had a longer battery life
Does it do what Negative sentiment ways Let's
say positive It's plus one Negative is minus
one You will put that sentence is what
Minus one like that You can see for
other ones Okay so now if I'm putting
that into my arm and okay I can
put the entire sentence Let me take the
token Says five tokens I've taken Okay so
now what I'm doing 55 tokens at the
time I'm giving it here So what are
the highlighted portion Can you see us by
five There's no speaks of rule I'm talking
about I can't give the entire paragraph on
this and I want to go on a
step by step Okay so now the same
thing's I'm putting it into here into yourself
Loop on Let's see how it works How
I'm giving Like I want to show it
to you in that I just want to
be taken example on What are the picture
I have shown it to you in the
beginning to slice that I would works the
same things here if I take five fight
takes here right I wish you'd It had
longer That is a five words that will
be the past input Second input will be
what I even very more vitiate had a
longer battery Then next will be what visual
Very more than next one like that It
can continue Okay it can continue for multiple
paragraph So what I'm giving here This is
the input I'm for the moment If I
ignore there is a recurrent connection That means
there is a previous state coming If these
are independent let's a sentence A software Words
Okay lets see that first Okay if you
see here let me see this one If
I take this as an independent there's no
here in the state the recurrent past What
is what I wish you'd had longer It
looks like a positive or negative ivy sheet
had longer miss It doesn't negative minus one
Let's say second one b she had longer
that is also minus one Whatever the third
one So if they ignore the vision all
it had longer But really the plus one
There's no doubt about it OK but is
it really the message that we want to
convene No So if I talk about that
the recurrent one what is happening There is
some previously than knowledge Also I'm Caringal It's
not like independent statement like this which is
like a nor any text analysis So what
will happen I'll expect some months like this
maybe minus one minus one in the past
Okay there's not much difference is always negative
but this one it doesn't look like a
completely positive one because I know there is
a history coming to that which is minus
one maybe inches off One It misses something
positive Not only completed but how do we
arrive at a point Two fights Not I
just want to indicate a number which is
less than one There's a company difference in
competition may give you 0.1 a point of
difference on the NEC Corp lightning So this
is just to establish because you're already familiar
with the sequence of numerical on even in
the text Also this kind off importance is
there for the sequence that it be made
Processing you can do with the CNN is
a good option but ah if I talk
about the city volume I have 200 sequences
Okay That's where it makes sense But individually
made it one complete The CNN is a
good one to do it but the combine
that in a temporal sequence sequence of images
to contain Good Okay you got the point
here So then I want I want toe
this one I have some good interesting graphs
for you were to make my point clear
Initially I don't have this kind of you
have only static ones So now I can
I have got in some other conference because
I e I haven't created all this has
there in this one I want to pull
together so that I can convey my point
so you can see what is happening here
This for the simple argument There's not complex
gates Nothing Ls team I have similar diagram
for each off the gate we each of
the formula What I wrote here I will
convince you in next one of our but
here what is happening This tell me is
just to describe how the arm and floor
will be okay Given most more or less
that Francis also most other place if you
want to for the record you can visit
back So what is happening here These like
individually What These individually the notes on what
are these I talked about Texan challenge I
jump it to here I'm condemning the same
example Look now you need to think from
you Say what other techniques you remember We
remember in the first class itself at least
I don't know the previous Bagram What You
have computer understand Only the world But I
mean the vectors OK so there are for
mattresses Sorry for text You house different methods
to convert it You know some matter salad
e one of the methods Your family by
this time the account back prize osteopathy If
idea what back also you heard off it
Okay Very good Very good So I eat
I do have 12 states If you have
some gap anybody will discover that But otherwise
I'm assuming it disturbs you know some kind
of techniques to convert it once you can
work that in the same sequence off a
33 worst year Okay Maybe the fact our
size I'm just giving this as an input
like this What is happening here If you
see Can you see the floor here The
simple graph This is for coming from the
You didn't state This is coming from the
oneness Txt oneness HD like this Ok this
is for the Lestienne formula Don't worry on
that But if you see on that previously
you how What is the input into rate
Plus by us The same thing works here
also But only thing is the wage part
off it Okay is there but input wise
It's not just only one input but it
has even though t minus one state Also
that's where you see the combination of all
that If you see the family busy looks
like family only is the same as activation
function off input into weight Plus best I
haven't changed the fundamental rule off that only
differences you have instead of the just a
simple X You even have a dependency off
the hidden sleep That's what I want to
show it in a beautiful graph here saying
that Can you see here this is coming
from let's say this is important That is
in the both of them are going to
an activation function and then that is going
to the next one That's what the sequence
of operations going Once you convinced Then I
will go for the next next point But
any doubts here let me clarify at the
station Ah so what I'm thinking if you
see here also right I'm giving input for
each north The sequence just like you have
I wish Battery Our next one I removed
I and then next by like that I
given inputs for each of them But what
is happening in each of the input Is
that below diagram saying that maybe I can
take this one This is the feud instead
coming from previous one This particular one Yeah
is coming as an input Both of them
are joining and passing through an activation function
Okay And then that is going to the
next input So when next to nothing happens
What This will be hidden value coming from
here and ht minus one on again Another
Ext Evil cup Both of them are going
again in this kind of formula Yes that
is also there Many people may not know
that I haven't started using those but they
have LSD Um has enlisted in the complex
thing land This is the simple recurrent network
off the state I haven't used any complex
Your 40 I You know we'll tell you
once This is that simple Weather forecasting are
simple sentiment analysis It can do it as
long as it were kind of twitter kind
of data I am talking about protection affinity
because we have only short sentences But the
moment I go for big paragraphs you can
see the complexity off it Okay That's what
I will establish now saying that the simple
record net what may have the dependency Okay
that's what giving you to the heart Wants
to get pregnant or different types but for
the basic ones as long as the distant
dislike you were telling an example off Uh
who are you OK Next My name is
okay Maybe this one after my name Based
on the gender it cannot populate he or
she So that means the distance between the
prediction on all as long as it is
less than they are in is good enough
I don't require complex memory Remember Gate there's
that are not simple Internet are good enough
But when I say the next one I
declared in this mine Okay So before I
go to the next complex issues and northern
and establish the point let's understand different networks
here Can you see the picture here Maybe
a little dense But don't worry I'll tell
you what the different types are How many
types Other 12345 1 to 1 This kind
of a vanilla more of Washington What do
you mean by that Exercise off input If
exercise off our port Some kind of major
classifications Okay here it is not even on
the nails It is this simple one rate
simple classification sequence or image capture index and
imagine or puts a sentence of what Okay
this is talking about Second is of one
too many one image you are getting It's
actually speaking in the image sequence Even though
I'm talking about here That may be a
good point off little after the explanation of
l estimate Like mentally because it's coming here
for image Capturing problem is one of the
challenge even that goes in many conferences You
can't play with only LSD More only See
CNN to do their mates crossing You need
to do the CNN That's what Your family
and agree with that But after that you'll
get a keywords And after the keywords And
finally the captioning meets What if I throw
some keywords Will you accept as a caption
it want It needs to be a seem
proper or it is the sentence Okay So
if I'm talking about this picture what could
be the caption people People sitting in the
class at N'dinga session something like the meaningful
sentence blue that CNN is not a good
player that CNN can identify us There are
persons Okay Are 10 people those kind off
objects it can predict I mean detect and
then classify those things it can do after
that we need to use again the sequence
off words as an LSD um identified And
for that you need to use again Ah
a list Um so it's a combination off
LST um on this what I was telling
in the beginning if I throw you some
probably little problem something like this you should
be able to tell me Okay some points
are coming now but tomorrow we need to
converge all that Okay if I tell you
new problems Yes These situations I will use
LSD Um I'll get these things Are this
situation I'll use an CNN We use some
new problems also but this is one clue
for you Image capture Yes So how many
people understand the question What you are talking
about CNN If let me rephrase the question
so that I'll get answer from you itself
the sea and then you face is more
like a true false type of a question
CNN can be used only for him It's
classic It's not for detection Is it a
true statement A false statement CNN can be
usefully misclassification Only three minute list is not
for detection False So the CNN wise you
can use the application ways It can be
a classification or object reduction Both of the
approach wise underneath Torquay's is no different Only
thing is the flattening and the last signal
fire and those things will be different Okay
so in this particular example toe just to
convict the next point for you is you
don't were not interesting The classification type The
picture was taken Captioning I'm not interested that
I'm interested in their objects What are their
from their rival The and I want to
even flattened and lose any signal it if
you If you can just visualize the network
there I will have some couple of convolution
layers and pulling layers and all to extract
the patterns Unidentified Some object Okay These many
faces are thereby Maybe these many female male
whatever it is to what extent do you
want to do that Okay maybe laptops or
that chase or that this and all right
So from there these things will be fed
into it Ls Team can definitely work OK
then that will give a sequence in terms
because it's trained already on the sequences when
the combination off laptop jail this a knowledge
kind of a classroom Okay after it If
he's not playing on that it needs to
depend on some other wiki or some other
repeats are also that public ap Google FBI
Wiki p A I still remember some time
ago I want to use it Were some
kind of a topic modeling Okay so I
have some key words coming from even the
basic clustering you're talking about some clustering the
great I'm not talking about complex deep learning
machine learning at all I have some words
OK Some kids and all the stuff Then
when I do a basic last thing I
got some even in came insults If you
can see their divorce who can identify I
don't know whether you really explore that Top
five Worst You can take it from the
uh for each cluster just exploded Really We
have not done that You know how to
do that Came interesting You did You might
have done already extracting it off I keywords
Now I want to combine those five words
and give it what I can give a
top key What has a label for that
So we usedto give that Let's say the
cab then Um travel days that are not
If I give it to a wiki FBI
it will do me the city letter to
alternated travel a transport That could be a
better labeling So I am talking about this
You know I listen my people faces so
you know you need to do this one
Don't expect to build a complete one network
to solve your purpose No no No one
can solve the real problem like that So
here is a combination off like network This
like we're talking about CNN followed the l
S P A Even LSD Um also we
didn't that place If you want some kind
of external a piece like this you can
use it okay And finally you need to
get yourself problems on That's the approach then
Sentiment analysis can be like this Cinnamon hours
were given a sentence Is class for back
So here what I'm giving so many Just
like the example What we're talking about here
I'm giving you multiple sentences Finally I'm expecting
a single output pass to sentiment Negative sentiment
that is so you can understand Want to
one want to many many to one or
know many too many mission translation are Now
that translation is I think I made no
mention the court previously from P J When
he mentioned 10 years of the research off
a Mel of the mission Translation is taken
or within six months by the deal Okay
so that's what the meaning to many I
am giving sequence upwards and sequence off Words
should come out Onda A sinker sequence input
output That's what the video classification where we
wish to label each frame of the really
Sometimes the sequence matters Most are more or
less but sinker sequence also asynchronous and synchronous
Not much difference They were depends on application
So in your real while you can map
So these applications if you talk about let's
say these other five different combinations available There
are five different data types are six different
data types For me you have your domain
export ice You may not feel all the
increased there but he will get there Number
of applications from them Are you with me
in that We'll take over some more applications
tomorrow Okay Now let me talk about an
interest of time He all of you and
appreciate the requirement for Arnold Now let me
talk about the limitation which will boil start
to the esteem type of network So there
you do have the venture ingredient problem Because
when you talk about single network itself is
has a vanishing radiant Even a recurrent network
should be a natural problem Okay then uh
this be pretty It is nothing but back
propagation through pain So now it is the
same approach What we are talking about terror
propagating or nobody deniability complex That's very if
you remember that time Yeah Not not Not
really Not really If you recollect what we
have told even that there is some impact
on that Peter to do every 16 at
all But I'm not talking on that position
Point here So here when you say vanish
ingredient when the error said What We're calculating
the single word If I am talking about
I have another function I'm calculating the partial
their way to off the terror function with
respect to the weights Okay So that I
am trying to adjust of age how much
it is deviated from the expected off But
that's what I'm doing So what did happen
is when I I was gonna cleared only
four and 11 This is Mark My input
is okay I have a partially weight too
And I got something like a 110.1 as
an ah fraction which I need to change
my input That is you after that when
a multiplying changeable you remember So find one
That's another point When Here find one in
2.1 0.0 on 10 for minus two like
that If it back propagate in that tent
layer in the beginning layer it will become
Let's a 10 for minus 10 So it
is not really adding any value changing that
rates That's what that even if you fix
appreciation level of repeatedly to 16 doesn't see
the van Children problem still will be there
cut off in the sense it is not
even meeting that Don't yeah that's what they
already do it that's what they're doing it
But only thing is as a network uh
you see it is taking a lot Many
pokes but it's not doing any change for
me So that's what if you remember one
bales discarding yet is with fixing number off
the books Put a constraint saying that Look
1000 books you run But sometimes I already
mentioned do If you remember it's not like
a small examples where it will run in
a lab like this one Convergence for us
Taken a week on a GP I can't
ah change one parameter in arms to go
for it We can come back by the
time of the year will be over without
any improvement in the model OK so we
should be smart enough doing that even though
because I know it takes that much time
to converge I should be able to do
identify the path from not fit How much
change it is happening so that I can
cut it forcefully and then change this one
That's what the we do typically there But
there is no single one answer for that
Okay On what basis Second I can say
one technique What we used to follow is
the change within the wages not more than
within the IT rations is not more elected
and bore minus five What import minutes We'll
put some casual Okay then I'll cut it
out after 567 Yes Yes you can change
that That is a different dynamic way off
changing the lemming right That's one impact only
I'm not talking about only one That is
one case Even in that case also I'm
talking aboard That's kind of an adapter lending
rate You are talking about their rate That's
that's okay That's one way off changing it
based on the idea being the pattern when
we have smart way of doing it But
I'm talking about one way saying that I
don't want to continue fired registrations if I
know that my patterns are not in the
way that it's converging within 100 iterations itself
I don't want to waste May 6 senator
conditions That means basically a pork sexy Okay
I want to cut it out and change
my tune my parameters and then run it
Italy stopping is for different reasons here Even
I'm not even converging it there Really stopping
is for one of the reason for avoiding
the war footing Okay after that okay I
don't want to again do it It's not
improving That's also not completely different But there's
a little different purpose Okay fine So then
uh results of the okay Using the BPT
is also nothing but back propagation through paper
The same concept in a time Dimension is
also there then Ah I know the composition
of million nonlinear transformations that they're okay So
here this established the same point in terms
of Okay the same point as USA The
clouds are in the dash Naturally consist Guys
not require much of them But understanding is
it's little of us Aziz the gap Gross
Okay let's say I am I grew up
in Connecticut I am fluent in dash Are
you growing up in Billy You're fluent in
national Naturally the Hindi will come but that
may not be true Some people laughing Okay
even I don't know Canada I'm saying I'm
from Bangla but I'm not I don't know
Find Abby Yes So at what basis I
can say Okay So the one I'm talking
about is here I will say based on
if I have only this much information But
im you need to focus on the dots
Okay So I may talking about my but
your own biography or writing You started somewhere
here and then you Then in between you
told my parents are from maybe Tamil Nadu
Or maybe your Panjabi or authorities So you
need to make an finally after let's say
12 paragraphs or maybe half per 100 War
under sentences you are talking about I'm I
speak fluent That means you need toe bring
the context from different sentences and then you
can predict it Something different So what I'm
bringing the point There is here and the
gap is small You go for a run
and you don't quite a complex estate But
when the gap is more How much more
I'm telling in the beginning off the biography
off such Angelica let me talk about he's
from this and at the end of the
bag of that book I'm talking about this
Maybe as the gap is that much huge
But can I remember everything in that book
and then do it Justice cannot be correct
So that where this this kind of long
short term memory kind of aspects are coming
here how to solve it the simplest ways
This is one of the pro TEM talking
about I need to help some control on
May recurrence Okay that's it She would through
some kind off it memory and get so
by default if someone asked You are in
and has baby for the memory Certainly no
memory miss where it is remembering I'm not
remember me I'm not storing that value anywhere
in the gate It is using the previous
one you had taken but is not remembering
rate What are different You are using what
two inputs off that you are using two
inputs and computing Once I go to the
next one I'm not remembering what it is
correct so explicitly now I want to introduce
themselves Okay so where That's where this complex
thing is coming up looks crazy But believe
me within 10 minutes you will buy in
what is there in that Okay So what
is happening here is it looks like some
complex Let me explain in a simple worse
fast What is happening There is my purpose
to come To hear it I need to
have some memory we can think of Okay
I'll put some memory unit and then do
the same operation Just a memory unit But
I need to help control on the member
unit How much to remember how much not
to remember That's where the long short term
memory it should be long enough Where the
same time Short Enough way because of other
competitions Because I can't keep on putting all
the junk there and then expect some good
results Okay you should remember the long sequences
but even it should be short enough so
that the memory will not be exhausted So
there's a complex problem So start with So
the simplest way to explain this is you
already know that come the basic what lord
Each one rate so Let's say in north
means what Activation us Input in tow Wait
Plus by a skirt Maybe if I put
in the same notation x w b this
whatever independent Nordea's So think that what you
are seeing here in the local Okay how
many of them we were seeing here Maybe
10 had you live it as activation function
But other than that three day signal place
it might that means it is a three
independent lords who can think off It's it's
a wrapper off my tipple notes a Lestienne
actually Okay there are other variants off it
And l s team also has some variations
of it Let's not talk on those but
at least to understand one framework which is
little advanced and are And then let's focus
on one version of a Lestienne were you
are talking about There are Let's even if
you consider the stand hitches Also inactivation is
also later We talked about that it can
be combined to dance but otherwise it's gonna
be dependent if you leave that there are
three gates Can you see that 1213 Okay
so then there is some kind of a
pink color is nothing But what some kind
of a simple point with concatenation are maybe
multiplication Something like this is kind of a
multiplication This is kind of it can get
national This is a local government This is
a simple oppression So and this is nothing
but three gates which we can't really confuse
if we call that as again notes and
all right We call it as a less
time as the north in that Nor also
internally we help three small notes but the
name for that is gets okay So as
the name indicates get that means it is
having it It can close the gate It
can open the kid so that the relevant
information can pasta But the gate means for
somebody that says there there are three gates
the names also I will tell you Forget
input Output These other gates gate means what
it is doing The gatekeeper job off minus
one plus one type of it But somebody
needs to control these gates to get the
final output Okay this is something like a
conveyor built in that top your c Okay
That is called a cell state I'm going
to just a lot of under But don't
get confused Within 10 15 years it will
be clear Okay as off now And I
have a beautiful diagrams to make my point
OK but at least now you just see
the static image Okay then I will uh
maybe because some places some off your little
blank first show the graphics Then I will
explain the concept But at least it Let
me just tell you there are three gauge
on Somebody lets it These are the gatekeepers
They will understand If you think the same
example off it I will take a simple
example to convey my point as well These
are Gates 1213 What are the signal you
can see here The sick model gates Even
10 hits Also kind of a good But
it is anyone activation not the company Yes
Onda signified all these other places It's kind
of a sigmoid Okay activation But it's kind
of figured and there is a cell state
So now before even explaining this nor let
me show you the graphs how it works
Let's see First one off them 11 night
I'm even though there are two Let's first
see one of them Can you see here
Small graphics going It's simple As simple as
that Is it first time Okay this is
forget Good This is input Get this output
here Just for the name sake on We
will understand each off them independently What is
happening here There are Let me repeat Okay
Just like B minus one or next d
passing Perisic my function Okay And then going
to his some point with operation Is there
anything crazy there Nothing Just like another man
You are passing the XP unhitch t minus
one on trace It might freak malfunction What
it will do OK you feed yourself to
sit my type off It may be 021
but zero on the hearts and minds Maybe
01 Okay Zero means forget everything one means
Remember that even if it is taken to
states that face called forget get out Maybe
Remember get You can talk about what is
happening there If you see that I will
convey my point in three days This is
a graphic Then the question Then I'll summarize
with an example as well Okay Is this
simple oppression just taking two inputs and then
passing tracing my function Now let's see the
input get it may look a little complex
but let's see Let it start Okay Can
you see here the same with you passing
through here It is going through here also
Okay And it is passing through to activation
functions One signified another one is manage Okay
So typically here we use not the real
because Ray Lewis has an inherent problem off
You don't infringe not controlled one Okay that's
one reason I could say that there may
be other reasons OK but at least one
of the reason even it is very good
for the scene and hidden layers and all
here I don't see a common practice to
use it That as a function sig might
we have a valid reason because I want
o get keeping Operation 01 That makes sense
Oh if we are If you want to
make a decision off work hard sick mind
and I don't want to remember um I
want to remember I don't remember if that
is the case boxes my type of Okay
So 10 it is the common one candidate
to another values between minus 1 to 1
That means I'm not doing a decision of
the road one but I'm giving it importance
of that Okay So the same Maybe this
little dark here but anyway this is nothing
But why did the previous cell state What
is the output from here Okay so you're
just passing through these two activation functions and
then you work on coordinating here that you
are passing through Actually this is Gatekeeper Operation
is what Nothing But it will decide what
to pass through But final actual oxy they
should go to the final output Can you
see that Outputs to outputs One is a
long term memory Other one is a working
memory Yeah even in the island And also
Hominy puts up there Is it one or
two are in and we go back There
are tools not not work But what do
we need to know Puts Because I'm increasing
complexity of the memory thing in that right
here there's no memory I want to understand
how much to remember so that when I
say all this I am from Delia Rosa
one so and all I want I'm fluent
in if I want to tell that somewhere
just like ever A human brain remembers that
context Somebody needs to remember the context Okay
That's right This one is a long term
memory You can see when it's long term
short term That means there that the reason
the name makes sense even they look crazy
Combination though one is for the long term
rather than is a working memory Yes that's
what the short term it constitutes I can
revisit also But let's see the last one
Okay Before cell state then output is what
the same Okay let let me disclose this
Yeah it is starting from here but there
comes here goes to a sigmoid function Okay
on it is getting another output from here
You see here that means whatever is updated
from the previous forget getting up Ali gatekeeper
job is what to decide what to do
it But final thing What is neat What
is persistent and long term memories there in
the cell state that what the conveyor belt
is doing this taking inputs from the gate
and then doing the operation That operation also
will go directly like this from the import
gate I for the hidden state Also what
about it comes It goes from another activation
function So if you see here Operation Ways
this input is going for it An activation
But what are the output so far from
these two How much to remember how much
to input that is also coming from for
me in a then hitch Finally that is
going as in working memory for me So
now I understand that you have still questions
But let's take an example that it will
be more clear for us So the example
is like this Okay let me start with
you This like it's convertible this much Is
that okay It's not like so we're using
two domes One is the gate operation of
the gate is what do you know What
Not willow but somebody else to monitor all
this That job is done by the Cell
state Okay that's what the top one you
can remember So now if I take an
example like this let's say okay the forget
it Simple right We're the same form life
Revisit later But what is happening There is
a hidden state or next e on I'm
passing thoracic but I want to remember or
forget I find it is weak No the
same Jeremy's Get it off this one minute
Remember this Let me take an example there
No I'm talking about something When we see
a new subject we want to park get
the gender of the world subject I'm talking
about two people okay You ask a lot
of questions you are working in this uh
let's say the insurance industry she's working in
let's say the finance industry that maybe I'm
just talking on 11 sentence each So I'm
doing the context switching so frequently Okay so
the next one should be here She means
if my in my memory if I'm talking
about something heat will automatically talk about that
He he's hard I mean not her He
his kind of thing will come So when
I see a new subject we want to
forget the gender roles of it So how
the things that happening there in the input
gate for sick mind innovates kind of input
layer which will decide what to do 01
type Ok then in Dan Hitch layer the
vector of the new Paradise City could be
added to the state that can hit I'm
not making a decision making I'm just taking
a value off the importance of the value
for this and then passing it to that
to inaction in the cell stick Here in
example of our language model We would want
toe add the gender of the new subject
to the cell stick in the far get
kid thing What happens Let's say I'm forgetting
for us time talking with a person He
that means that needs to be forget from
this That will be zero Lexi No I
need to add the new subject That new
subject Maybe again He or she person he
er she whose responsibility is that That the
response will have the input gate We would
want to add the gender of the new
subject to the cell state to replace the
world one with that you want Okay here
we are doing whenever there is a sick
mind I'm making a decision whether I want
to ignore our continue in this battle I
guess I want to switch So naturally the
Far Gate Gate is ignoring It is output
zero on input state It is adding it
There is a lady OK so use the
new subject as and so that you can
predict she for the next one What the
celta a less team is Cell State is
doing will know the job off Forget create
an input Get is to say that it
is zero is the one actually operation It
is not doing investigating the things and it
is in putting what the new object new
subject is the actual operation off that is
happening in that cell state That's what it
is happening now The last one in this
one is the open gate Now we need
to decide We are going what we need
to put Haman outputs other Here you're puts
one for the long term memory Okay One
for the working memory long term memory and
working memory boat They should have impact on
the new subject or not because I should
share the subject Okay so the far get
could retake it off Okay ignore the world
One input get will push in that OK
there is a new object that will need
to go The self state That's what if
you see the Self State one Okay if
you see here What about the operation that
will go here directly I'm that well even
again Commits it and hedge Want to make
an entrance Even in the rocks One put
gate in simple words in the Okay even
that's looking like very complex What I'm doing
is just a sub Texas off Forget I
I for input for forget Well foot open
for each gate the same formula what you're
familiar with I just added because for Alan
and it is very common to help input
and even the hidden state only thing I
added the notation Why this if off here
I or you Okay so in the far
get get even is you can say it
an opposite way of telling it Just telling
me how much to remember together they would
find it Which is easy for you in
case of I How much to input I
want him put only he or she has
a one object or maybe a little more
than that Then output will tell me Okay
with this one taking care of our to
remember what too for Greg How much to
focus What should be my focus So that
now the focus is clear Okay I'm not
again worrying on whether he or she that
is taken care Now I'm talking about the
next sentence Okay He wants walk Maybe based
on the context I want to predict that
one So sell state Anyway we have something
and there are another important point is in
our own And we have only one output
here we help What long term in long
term memory and working memory That's what the
justification put long short term memory is more
like a long term memory plus start on
memory Both of them are coming into the
air And l s name is a combination
combination of the words You know you can
see one more time The graphics We talked
about the questions OK we're talking about 11
example off the changing an object What importance
of each you know see each one of
them one after the other How many gates
we having This one in a less damage
Someone ask you how many gates you are
familiar with You can say the watch The
simplest washing has even they have something Input
modulation gate Some complex things about some variations
of it They don't worry on that It
is the base washing which you are familiar
with The L S P m At least
three gates are there So there is another
variation off it People call it as a
G R u gated recurring unit Okay I
haven't included that But just for as an
add on why you need to get three
gates do the purpose with two gets some
optimal What Sanofi handle there Then that's what
the gator they also have the same concept
of memory are now instead of three Great
dick fused it into nuggets but that is
a recent one People started using it but
more prominent one At least you should be
familiar with these LST if you're comfortable with
this And if you want some performance improvement
if it is really happening then think off
that ji areas it take of it even
if you don't get no problem at all
That's why haven't yet any slights on that
Even I have otherwise comparison off all that
and it will be too much Okay so
at least you should understand There are Gates
and the Russell states so that sell state
eminent somewhere Doctor Bills like a conveyor belt
Okay where it is taking from this gate
which is getting filtered What needs to remember
what needs to forget or what needs to
be the new important That's the job of
the gets Once the gates other somebody needs
to take control of that That's what the
cell state is taken Care off So if
you summarize all that in this particular case
just sell state This is the new input
And is the hidden state going forward Ready
is what sigmoid advantage Sigmoid So that is
remembering whether zero or one type of it
Okay after that the same input is passing
through the input gate Okay here so important
is here right You're passing What off them
here That means this will tell me the
same input like this Okay we're that I
need to forget about this one And this
will tell me what is the news New
object coming into picture Okay on Both of
them are combined here That this passive directly
as this self state Okay Next day I
because it even it has another impact even
on the output What to focus OK what
to focus means justice the same thing He
or she will be taken care here as
a But after that I need to convey
something right as a prediction on this one
that was taken in the working memory Okay
so similarly the Votto focuses coming in here
So one the sigmoid function One time hedge
function Okay if you see here sell state
It is getting one operation here another operation
here which is possibly ethically like this and
even one of the operation here as a
baby So it's a little complex I do
understand But still if you remember the pictures
at least it will be able to convey
the point Ok but nobody asked you internally
Okay Right Millie Equations and all But these
equations are wired for us at least to
understand It's a logical sequence of whatever I
learned so far Even if your first time
if you see that little complex or not
for anybody even for us also Even when
we learned nobody doctors even this kind of
a self learning plus when we applied and
not if I have time I will show
you crazy Lee visualization off a Lestienne looks
crazy because it's a recurrent itself is crazy
Right Okay So because the visualization of glad
Cam itself is okay for you were ableto
appreciate because the simple 07 and all very
this concentrating or not But now if you
think of this one how crazy it is
I'll show you one depends on time How
much you will have an example on After
that we'll go toe the visualization The open
source from Harvard University It's a good one
You can spend at least five minutes and
spend the rest of them You can exploit
it for that Where you got the trucks
off that Okay fair recap Those points you
need to understand Okay I think Slights place
I have that much All the living What
we heard in last 40 minutes of this
one is with just one mortal daughter Enough
Palestine will do that Okay But we need
to understand why we need to understand this
Gates days that on all is just to
run example That's what I was telling you
Even the teenager also will be able to
do because you don't need to understand all
this for him What is a weapon so
scored until they make sense of that OK
And they will run it That's it But
what we need to understand this one is
whether l s team is going to do
the good purpose Aji are you or maybe
well how much we need to use What
How What kind of your more think from
a design perspective Okay what kind off layers
like I need to concoct Nate How many
listing is not just only one LST maybe
multiple extremely layers to do any toe pageant
for my purpose Those are the things we
need to understand what is happening inside No
Ah that's a good question So somebody asked
me in this one You are not telling
me how many sentences I need to remember
are What is the contact I mean tested
in his complexion I'm interested in his speaking
ability I am not talking on all that
But how do we how we learn what
to forget What does your wallet accepted Ocasek
minus 01 But how How it will look
during the training phase What will happen I
trained with the training I'm not just jumping
into the real world and and doing it
that responsible of the training years This is
the entire document Your biography or my brother
visited one of little matter Plus I'm giving
an important It is I'm learning on top
of the data Okay so that even in
your talk about word word prediction of this
one eye trained on something there Okay son
raises in dash Okay even we train our
kids to tell that once somebody told you
similarly the training should be there under common
sense and all this one because it's the
one leading missing for us in the year
we had telling a body a 2.3 point
you're wearing What is lacking is that common
sense once that is there like in movies
we need to fight with them or we
need to work with them Other boards of
software towards aerial robots type Okay but it
takes a long long time to go there
But what is happening there is it is
that I can't hard fix saying that you
do this You do that I know But
you have been told to give the data
that based on that little the entire interface
whether I need to remember the last few
wars at this What Remember not that smart
enough there You're not hard coating anything on
that That's really smart Okay When you when
you peel it out it is not as
in judo at CNN We need to spend
some more time Okay so but I thought
Okay two of us is the one which
we typically spend because either they need to
cut on other topics OK but if they're
really internalised Jamaican spend I have much more
Another one to about a meteor London to
cover But at least I should you should
be getting is the best one Okay that's
tomorrow Not parking too much Last one No
So I really leave it to you So
you want me to tell you were interested
more on the This is a major text
or the CLS team I don't know I
mean just even in the jewelry we can
talk on that Yes I have one example
there Okay but example Well because after talking
all this complex it it will be one
line and then running it Okay because that's
what the disciples saying CNN At least you
have some additional layers and all here I
have talking about when you last won over
I'm talking about only one layer We leave
morals out at the same thing What a
lot Add and tell You have control of
telling whatever there is this heart sick mind
of normal And then you have control on
that And it was about us OK but
once you take a big complex operation okay
then you have control in terms of different
whether I need to put an estimate What
layer Okay that I'm talking about a small
beginning or middle lottery Maybe multiple it That's
that will be good exercise to do that
Well done One example Because I talked enough
I need some break from you guys also
Okay so well done example Then we'll come
back to that But are you clear on
at least three points We should be clear
Our fundamental thing off while we were using
that you should be clear while you were
using armed and should be clear and the
same thing where we need to use their
less time kind of thing Well it is
complex And that time was also competition also
will be more for this but we need
to arrest him Always super past the our
name always Is it a defector Standard did
not be Because if I talk about that
the gap between the prediction business purposes small
I don't require this complex less time stuff
I can reduce the fact rough I mean
a fraction off operations when it when it
is normal to say are in And I
heard these many operations to do that in
the list Him But in gives a fireman
maybe here it will go with instead of
excite comma maybe X'd comet w sorry HD
minus one That's a So how much of
a graph Magnitude The competitions will be different
Ah at least as good as as good
as Ah yes yes yes It's more like
Okay Even with the small it is a
saying right Even with the small Uh don't
you This monster given for Ah what a
Something was there It's like a big stick
Okay if you want Oh yeah Some place
it is like there You can't do it
anyway Here you can use it But only
thing is different That's why these are like
we're trying to give you the building blocks
for you but as a because I am
working on the time trying to get a
pen with the vie question also Otherwise just
to learn what is an estimate and nobody
they require as a person to teach you
Okay a good amount of videos good amount
of information is already there So that's why
we try to make it interactive and then
make it coupled with example so that you
were thinking is it will resistor in Norman
You know when to use one that's the
should be a cuticle it That's why I
always don't miss on my part Really What
part will not suffice And I don't require
Toby Ah you statement our teacher for you
guys Okay so the main part considered on
my part and even if you have some
gap in what But you just read some
in material and all you can use it
I have enough references there Okay Otherwise the
other Yamil batch for each of them They're
talking about 16 16 hours CNN 16 hours
autumn in 16 hours It's somewhat examples We
get some something like that Okay but uh
I can I'm ready to share the information
What are you have when his differences Other
ones What other material How also have little
more than this You just showed up to
me and I can share it to the
guys No problem on that There's nothing like
a proprietary material And also I don't claim
everything on that So maybe there's my style
Maybe unit for me That's it Otherwise it
is Ah every material is there in that
on the most level Google I won't take
credit for that Typically leave the differences okay
So that you can even that beautiful glass
what I took I haven't used for maybe
23 batches before Enough My friend only used
in one conference Then I took the difference
problem and then I started using it Okay
so we keep learning from there and I
keep sharing that information That's what way should
be continuous Laettner Okay Now switch to the
uh example Okay so if you can you
open ever uh laptops So what we're talking
about here I have two examples Okay Run
from the text point off you Let's see
if we can run Maybe Let's talk about
the step to in the deep deal experiments
So all of you for your information the
same example I haven't shared any new examples
for you Okay The same information What I
shared to you is what I shared to
you guys right No Maybe a month ago
This what information we shared to you Okay
this is already run the step one Also
we run first Example still the second example
that will take maybe hardly fight 10 minutes
We'll see when we can run that Let's
focus on the step to in Steptoe also
helped two examples One for CNN which we
already run it But for the C part
10 data set Right Remember then the second
one I will give a brief introduction for
that It's a really use case The CNN
example don't try to run again because it
will take a lot of time to Lord
And all because of computational complexity will be
more than that So we have 40 minutes
Okay So all of your with me till
despite okay I'll talk about something on them
and they'll be some if even if you
have some gaps and it's my responsibility to
matter to fill the gaps Okay Ah but
here also I'm assuming some basic see as
you you will be knowing And then we'll
start from there Okay All of your between
this Your example Uh so what you need
to do here in your abortion Something like
images there Can you see here in this
one marriage Miyajima Just there In your case
that won't work with you in the latest
washing because that is duplicated Okay just remove
image We don't want that Midge Now okay
There's only one chain you need to go
on But that's it Let me tell the
context for under this has a dependency off
one file Can you see that Fine somewhere
Can you see this one Text to data
Nazi Yes we do have that file with
you I think I already shared that So
I love you How run they were record
Aston Supply You should not get any of
those other snow I'll be surprised because we
already learned to example this the third example
kind of stuff So all the those things
that should be taken But if you have
some issue we can talk offline in terms
off those installations or anything like that So
can you see this Ah data The data
is little dummy I mean Dominus is a
creator Data about the application is a real
world application Can you relate to what This
day Uh by seeing the structure of the
data what it is we're from we get
this kind of stinks Some kind of issues
are ticket There's the bread and butter perrotta
in united industry So you can apply to
any company or any Klein Doesn't matter This
is an internal directional Correct That's why I
took this example on so many people extended
this also in your previous batches that is
very common And then due to do this
one right So let me give the context
there So how do we get this one
Maybe you're jerar service No snow Whatever Whatever
The river Proprietary one You can just don't
Lord on a particular category These men instance
our issues out there Okay So what is
our interest is I'm giving a small example
portion here but this is this is inspired
from a real world product where we're talking
about maybe some 67 years old Example this
or not Maybe five years old Example This
one But that time it is very new
But now it's Ah democratized already So our
aim is if you want If you're if
I'm working with one of you We were
playing Let's go Mike Lane and I want
to identify the tickets What I can automate
from you Not right now I deployed five
people to solve over issues Okay Now your
question is what kind of innovation You can
bring it to me I don't want five
people are my sns are violating Okay So
what is the natural option for us Look
if people are not getting maybe even I
need to have three more people But this
is that that that was the trend couple
of years ago No What is the trend
I'm getting the problem What I'm saying I'm
working I'm come coming to you as a
friend or your client is saying you are
Your boss is saying to you I want
to improve messily That can be on situation
What I want to reduce my costs Do
what Other technologies Not I can't linearly add
the people and get the thing Salt that's
what the real situation we're in So what
other options for us Apart from adding the
new people we need to use the technology
ways So one approaching that one is we
want to Deepdale slice and dice using this
animal type of things on But you can
question me you already How Why did the
type of categories subcategory sub sub category Also
it is their most most of the cases
Not like I won't expect the medicines is
to be done before you do some text
processing on all Already The category means what
may be here I'm talking about software software
made then weapons old job are dot net
even in Java whether it is a our
on time on this one I can go
to that level right already That kind of
categorization is there in those minute tickets are
you can tell anywhere how many tickets I'm
getting for open source related requests I'm getting
the same getting for Java related requests How
many tickets I'm getting for a particular application
Is it the real rocket science there There's
already available but still why do we require
it expressing here Just follow me looking more
because I won't talk this kind of example
with an impromptu students because they don't get
the picture of the industry But you are
ah having good experience right So when I'm
talking about I want to The problem is
I want to identify the good automation cases
so that I cannot flow to something toe
there a book that stuff get over Okay
so that the people can focus another important
test Okay I can tell you some example
if you're still not convinced there are At
least I was in wall in thousands off
script okay There were able to move hundreds
of people in out off their day to
day activities They were used for some other
purpose Okay It's not just only 12 cases
that starts from simple password reset to granting
access in Oracle system or changing the permissions
And ah hey John System on a separate
system across Okay It's a great application This
one and only thing I can give the
names of the client That's the only only
thing Okay but across the domains No in
your organizational site because it's a little old
application I'm thinking already some automation might have
already done I'm not questioning on that but
still there is a potential to do that
So how do you identify the cases We
thought much manual airport so that you were
technology of the planning on mission and he
can play the role That's what the problem
we are talking about Is it relevant And
we're talking So but what I'm talking there
is the category and subcategory I can say
the look a job category off the subcategory
There have some 300 tickets because a lot
of volume is there Oh care to make
that one Can it work that way Are
getting what I'm saying Before you classify something
on this decide off this labels I already
how categories in this one It's not like
a dump start for the organization There reach
organizations I decide they are medicine from last
couple of years but I can't use that
directly for my automation journey The simple reason
that I can give you is some may
be generated from our progenitor from Mr What
Some tickets some may be a root cause
may be different because maybe what some physical
disconnection happened from May in its are because
of the root cause How many tickets might
help come there Somebody says that I am
not able to access the DB Somebody will
say my app is not accessible This is
Don't that is Don't that may be boiled
down to a number of different categories One
for database one for Web server one for
absolute But the root causes what physical power
issue or let's say would actually be crash
not the physical related something crashed our memory
Some bad jobs were getting um maybe failed
though memory were consumption happened Okay so all
this this journey started for my ps What
I'm talking about all real examples That is
where I am involved personally Also where we
started with the static off started Piccoli identifying
based on the statistics for these other cases
Some other cases even we done a proactive
thing Also in the sense First you identify
what the problem is if you can automate
it Okay Let the problem happen and then
I will correct it automatically I don't think
what a human intention in prevention The other
thing is if there are something like a
bad your failure and all this one Why
should I wait some bad job to get
fairy already business this country happened Can I
Do you protect predictive action with all this
and anomaly detection and my all Maybe she
learning deep learning and we have corrective action
And then and there itself So memories or
shooting Okay so I could predict it that
it is going to fail The magic is
going to fill if a don't add memory
there Okay So long that the vendor and
then add that and they should be accountable
for all that I can Okay Just like
i an indifferent and add the memory And
then who is accountable for that Everything is
documented again Lager But they're also you can
question me saying the article you may not
accept directly my statement Already there are so
many serverless Ehlers are you talking about some
rocket science here already Start where there are
so many up APs on the stopwatch with
HP or Dell and all those guys If
the memory is overflowing off 90% do we
need require a deal to do that 20
years before it so they can claim that
what What You are talking about I have
some a large to do that Then what
is the value we're bringing here Am I
making sense When I talk about I'm giving
you that hint and I'm giving you the
challenge Also I buy in some point in
the interest of time I'm not taking too
many things from you but because we need
to complete it But the main point there
is if you remember any time under service
operations in this one it's kind of a
big kind of a cricket match day perfect
in the big Wall you will help thousands
of Star Wars and this is people will
be crazy looking at doors and where the
issues are their color coating on all that
If you ever seen any operation center Okay
so there the typical poor guy will get
thousands off a large walking it If they
can reduce the 1002 Let's 100 I'm making
his life better so that he will attend
that those say 90% off Making memory is
not a big deal already How they let
for that but like that I'm getting hundreds
of others How do I know that Which
said what I need to concentrate on If
I follow the patterns there on a weekend
there is a job happening on a regular
Regular job patterns are happening there That 90%
is a real a lot for me but
it's not about one What sort of what
it is a regular pattern You ignore it
so that I will concentrate on the sorrow
when you stopped other servers I'm trying to
I'm just not adding so much of value
Just eso what you say the new thing
But I'm trying to do it in a
dynamical fashion Not just only one parameter taking
parameters from multiple things and we can identify
using this machine Learning or deep learning the
patterns makes sense So this is a very
big application where I am talking about all
that for fighting minutes is you can relate
that wherever you are connected with Okay it's
not like you need to do the enterprise
uh gamut off that applications in that Okay
they have predicted applications they have Even now
you need to identify your organization where it
stands for Then you can define next step
You already done this one Okay Miami's I
will take the challenge of this This one
automatically distressing monitoring one I will reduce by
10% Even that will save lot off time
for the manual effort Okay You can take
step by step No that's the background for
this And now even where we start in
that is you already have labels here Can
you see this This labels are actually step
Initially we started with the class doing kind
of a problem because I don't know how
how many clusters toe do the automation Okay
And that do not Based on the category
I am taking the summary and resolution those
kind of things and then group it Okay
And there's a manual afford their initially we
started with clustering and cluster evaluation on all
that Finally we don't some manual effort also
finally be regulated Some some labels Now what
I'm talking about here is how do I
use this Okay to do my part of
the classification kind of simple classification with the
text later So let's see that example Then
I could take a few more questions Okay
I'll if you open that So you go
line by line there Okay Some of them
are very easy Okay Let me see anything
which is not acceptable Important this particular case
for you You see I have a glance
at that Anything new that there are some
things new You should tell me What is
it You there These are all justice system
logging and are less Not much difficult I
hope Yeah Here You know what sequential and
mortal okay to approach is okay We'll use
one of them here Here you know activation
dense dropout Okay Flat then it is all
there in the convolution We don't require all
of them at least for this particular example
That's okay Made you just remove it That
is duplicated It's not required less than me
maybe And you want Got it So these
are utilities optimizers around us then cross validation
is all the existing ones right from your
machine learning you are doing this No Tell
me the next function What it is doing
Is this the function It suddenly did the
function Name load data from file Okay maybe
I'm using that Linda text underscored Data RCSC
I'm using it But what I'm interested in
that I am interested in the somebody I'm
output columnists labels That is that in next
1234 on Magnum feature some 300 I'm taking
Okay so now something new maybe coming So
in a sense I'm just learning the data
using the pandas So I'm taking the summary
into that And this is the one you
should tell me If you're familiar with this
what I'm doing there give me contact place
that are about to work or be afraid
of it What did output from here And
you see Theophile de office converting that into
what we call another world M bindings OK
even if you have some questions tomorrow while
I'm talking about Annabelle just recap If you're
familiar with this I'm happy Then I may
pose more questions in the real world Want
to use when to use what That should
be over Taken apart Okay I don't want
to repeat what you already know We'll just
start from there Were you very what understanding
is but at this stage Okay you are
converting it into that world and weddings I
have the victors No that's what the Okay
Here it is Actually the president actually thing
is happening The conversion is happening here Transform
that I'm just converting it into an array
Labels are already there I'm just including that
because I was 1234 I'm just including that
there That's it Okay As a string tape
I'm talking initially What is that name 1234
Is there right label index I'm just making
it to dress Drink That's it Can you
see that Ceramic training parameters Bunch of parameters
So this is what we have seen Reality
These harmony among examples were run by this
time I think to reality So initially I
don't have this kind of a pattern of
defining all that If you see the first
example I just want to make sure within
finance of pork and ability Plenty Now we
crossed that stage Now as a standard practice
we need to see define always good to
define the parameters at one stage so that
it would be easy for us because the
python is very popular and do but it's
very crazy when it comes to the liberty
If you don't do the comments at the
same time if you use that you can
define parameters anywhere I bet you if you
do that you were on uh project what
They were done here If you refer after
six months you don't know anything by you
I did all that Okay So make sure
you have a habit of at least what
That What does that mean on follow the
standards That means to keep all the variables
at one place Simple things It is a
good practice for your ways Okay Even we
have nightmares when somebody's debugging fight on somebody
else's scored will be a nightmare I'm telling
you Okay difficult And the good good helps
We do have some good practices They follow
on their documentation That's why otherwise if it
is just namesake somebody uploaded We can't even
to change one line It takes a day
Okay so those are mostly I'm not talking
on this one because if you are not
familiar with any of them you should tell
me where the magnet creatures by the way
you remember the deified If there is a
document and then dems Okay those terms Yeah
Okay So I'll let the dimensions and all
OK now here If you can see this
is not this should be very familiar for
you What I'm doing here Just I'm calling
that function Lord data I'm doing It s
train split Okay How much of that data
District 50 50 But difficulties to 70 30
or 80 20 is the standard practice Okay
that's it I'm not doing anything new here
and I'm printing that weather is coming on
earth Can you run under See that they
were able to run that just removed Imagine
they should be able to work I'm also
running not in my mission because I took
five minutes before the classic around 90 clock
to run this one and only ever Like
what is only the make Because I'm also
hoping the same washing you are using if
you get any other arrested Me Sorry Were
able to run from this point Just you
loaded that turned into that And now you're
building immortal Well done One example Add a
graph on all their with the original We
don't require that is optional one for you
because we're concentrating on a listing because I
want to show you even CNN Also we
can use for this data but whether it
is really good for us or not It's
I'm not talking on that front Okay Is
not the best produce an architecture of the
father network but we can use it Even
CNN Not unless team for this even for
the next data That's what I just want
to show it do OK so in the
test of time that I will give it
live it as exercise That graph can you
see here Can you see this lane here
Can you see this length I come toe
that line but there is only one LST
mu added But typically even a graph means
I created a sub graph using a CNN
in the water portion off it Okay but
they're also some midges used only that one
So we need to do And you can
this comment that I will tell you if
you want to run that also But at
least now everybody should before we break for
lunch you ignore the graph benefits commented on
anything even in your court date Got it
So at least let's understand the simple structure
of this with one unless tm on how
we display Then we can take another approach
off that as a graph as well Let's
see from here you are able to run
till this Fine I love you Okay No
I'm just printing the dimension of the input
and output Okay this is where the network
will be Okay maybe one month before you
have seen I don't whether you have really
put it into after that but let's see
how much you can make sense in that
There are three actions happening there What is
this first one I already that they're way
off into the sequential way There is another
way also using direct mortal But what is
the advantage and resident disadvantage Or maybe what
is the structural difference If I use sequential
I can go on add add Add on
all I don't require to mention anything after
and the end off it in case of
mortal will be more flexibility But I need
to make sure I can't hang any earlier
uh hanging like that Okay I need to
attach it to something This will be automatically
if you don't have any specific advantage to
make it into modelling undertaken by default You
sequence that will make your life simple On
the sell side Another example to show even
if you have some sub graph you can
create it Separate Linden added heroes That is
also possible time talking about But that's OK
You leave it at this stage have used
them bending previously I think I used Victor
in the scene and having used it embody
even if so typically the amber dating will
be one of the initial years The reason
is you're converting it into some kind of
transformations You want to do it Okay those
we won't typically do that kind of embedding
in the middle of the layers in the
beginning of the layers If you want to
do some kind of tweaking in terms because
here I'm talking about taking the max features
and reading dimensions I'm talking about taking that
input right So that's what you will take
it as an embedded Lear But others you
can think of it Input from me Transformer
input American talk about Okay Once I read
that Leo OK then you can you see
it drop out Dance dropout dense dense than
a couple of against And I have one
ls timely here on one of the parameters
For that I have output dimension or put
dimension should be equal to the previous dimension
Right That one Plus I hope to activations
One is you should tell me activation and
inactivation where you're required to if you remember
the picture Ah yeah That's not actually sigmoid
Look at the standard practice is actually an
itch Yes maybe I made her run some
modifications to do that And I'm I don't
share that hope to you guys because difficult
even help time It's not just only reading
or the court We need to change some
parameter and see the impact So that's no
no no no no You can this back
That means you just put that down hit
and then let's run that Okay So any
questions so far in this one these other
glamorous which you are already familiar with Okay
well anything I'm just telling about it In
all case What exactly Even if you don't
say actuacion in this case can it take
Can it work or not Yes I know
If I don't specify activation layer can it
work or not Yes it is required but
you don't require compulsorily to mention it It
will take based on implementation may be real
or something else It will take it as
every is not implemented But if somebody ask
you we need to compulsory mention it's required
compulsory But whether I need not mention it
because it will take from deport Okay similarly
even the Caymans where the compulsory parameter needs
to pass there in the K means clustering
You were talking about that What is that
Compensated parameter need to pass But just try
in one of the second implemented you don't
require to mention even that also did some
default but doesn't mean that you don't require
that you need to request K That's where
the people will spend a couple of days
to identify the optimal K Okay it is
required But only thing is if you are
not mentioning it is taken care on the
lip Otherwise it will throw out another fun
Oh now you you done your structure till
there's no only structure Is there what this
company is doing Actual mortal off telling you
if you're talking about right now I created
the skeleton You know these are my layers
This is my property of this Now I
need to stitch together and say this is
the forward part is my last function This
is my optimization that I'm doing in the
combine Okay So I'm telling you use that
data I'm interested in accuracy I'm using this
categorical class underway isn't adequately cross entropy Mighty
class Okay cross entropy classification on that time
Music added me because I can't Even though
it is numbers I converted into categorical That's
where he discovered that you should convince each
and every line why I'm using it if
you're not convinced my duty to convince you
Okay Clear So far let's run that That
won't take much time The reason is I'm
just compiling it I still not done any
free tunnel I haven't ever done there in
that line Okay so you just see l
s d m No that thing is happening
in this one Okay In this 45 line
maybe it looks like it tonight If you
ignore the print and all it's hardly four
filings Okay So what is happening there The
fit is similar to ever machine learning Also
only thing extra here is that structure and
complain once you do it Similar to ever
fit tender transformed Okay so I'm passing extreme
by train and passing the bad sides as
well Okay And I'm just printing the number
off it books and evaluating on Extra way
have the next test I mean split We
have the night 50 50% Can you run
this You cannot get the best performance That's
okay It may take some time Not too
much time I got only 44% that I
expected because it's not too much of data
That is also small example for you guys
It's time no one fatal could still be
hurting Okay The next example is optional but
I just spend some time But before that
this is like compulsory one You know what
We're wrong We run in the next example
and we have used even L S t
m and then were able to run it
Okay The other people who run come just
comment out these three lines then you should
be able to run these Also I'll explain
that any but that is optional But i'll
if you should be familiar with the three
examples below you can ask me any question
Still up first example we took with the
What be my Indians Data set Simple data
set Second we run it with the ah
say for 10 CNN example This one is
with an LSD um text data And I
have one more example of the rotors that
I can even run today or even tomorrow
Second with this example in your own it's
easy for you to go under A major
part is one line right So because it's
depreciated you just comment on this one The
defense condition this personally fault one Then you
should be able to run that are put
it we have because it's not the best
one right So what will you be able
to run it at least But my intention
I will tell you what I am doing
This one is there are two purposes One
is you can replace that Al STM with
some kind of fit Group off net Leo's
office Ian and I'm creating a sub graph
with some couple off CNN and CNN means
that convolution and uh pulling leaders I can
create as a sub graph and attached to
the meeting graph That kind of practice I
having been previously it is a monolithic when
you were building it but I want to
show it to you If you help this
going nowhere I can make the other so
I can make a less time layer layer
as an option I can comment it out
and then push this one there The performance
will be different will be the But I
want to show that one Because in examples
my concentration within the 60 within That was
what we have I can't help you build
that What you say the optimal network I
can take only one example In that case
I want to show different flavors Then we
can see what is the best network within
that That's what the performance ways I won't
expect a twist Commodities that will get some
1990 final because a small one But this
one data itself is not very complex right
I used to work with tense off em
visa files Actually the fight Same for what
I've given you 24 kb The files itself
though on Magnum peaches will be 1,005,000 kind
of stuff It took a significant amount of
time Only thing is I can't use that
data is not my data I'm not working
with that company also but still I respect
I p off that But what I can
share it with these Whatever I can extract
it out so that you can use it
in your regular that much I'm doing So
if you want to run this example the
same thing you want you'll just to see
some convolution layers in the loop we're doing
it Only thing is just put some outlier
as well and then you can run it
and then you'd use it as well Not
much difference in the performance with me because
it's a small later not difference because the
data is looking like only hardware software There's
not too many words right But you can
apply Just think where you can start it
differently Talking about big journey There you may
start with the basic thing Or maybe it
is advanced stage of production are predict to
level You see where you are and you
can use this gun effects Extend this example
toe your purpose because only we have some
time 15 minutes So I don't want to
start with the new topic but we can
use because we don't come back to again
The basics Okay just so far what we
covered If you have any questions and all
let's discuss that Replace the time Then tomorrow
I will start with small NLP stuff We
condone one example to be more comfortable Okay
then I will talk about the transfer Let
me Okay That is also primarily very important
You We haven't used any example of transfer
living but we're talking about building from scratch
But when to decide from do we need
to really start from scratch or can I
start from the transfer leading transfer Leveling else
Okay get the model But how to tweak
that so those other things will talk about
Then we'll we'll spend an hour under our
train quarter because it's more or less We
spend more 70 or 80% of time on
the surprised but we need to give some
importance even toe then supervise I'll spend another
Okay And we have an example also on
that Then the last one number we will
talk on someone wants topics if you can
grasp it are more on the practical brother
stuff E can customize that That's not a
big deal Okay this I haven't seen this
one was from is not my medium I
was thinking we're from things that honey please
Is it okay Okay Okay Border of these
image farmers are features when to use What
This feature stay perfect is just for the
hard this keyboard shift when to use or
even I just need to check on I
have some knowledge but I don't want to
give off baker information Let me look So
your question is when to use what This
safe dirtiest and stuff I got it Just
let me know down my list till the
point One of those safe 10 What else
So now a two leased By the time
we finish this particular session you should be
clear On what is CNN on the basics
of that And what is the how we
visualize the CNN And now with the last
five minutes I will use that Lester Mutualization
I was talking right Weaken weaken Do that
But before that you should become that is
optional for me Haven't promised that but because
we have some by 10 minutes we can
use that You will enjoy that But before
that you should be clear on that So
you are in and L s d m
Also it looks complex but it just think
from the If I asked somebody is my
question off it If someone asked me What
is the last time that you need to
use it L s Team is a kind
off but I mean is a variant off
ordinance I didn't and don't owe memory when
I require this guy As the name indicates
long short term memory When I require to
get the gap within that What are the
word Ember Texas Huge If I know that
the northern and cannot solve the poppers So
I request some kind of a g r
u r LST um in a less team
I will help three Gates the based Washington
Forget input and output Each gate is nothing
but not the complex One each get itself
Is that small Nora Nordic within itself Forget
will tell me as it did a sigmoid
function What To remember what to forget Input
will tell me what the same information What
is the hidden state from input state Plus
what new information I need to embed give
importance to that And then output will tell
me what to focus on That on Self
State will help me to do the connections
like a convertible That's it There are three
guests each Get this kind of independent A
north just like each note has a function
of this Nothing different Okay on We typically
use Tan Hitchens sigmoid for that function Okay
that's it for their less time And what
are the use cases You can talk about
speech music or more Member toward uh text
like this Okay Our weather forecasting any off
them So when someone asks Okay I already
know They remind all by doing equate to
use in esteem That sounds a fair question
Then on we talked about I haven't made
it much complex Even we're talking with and
symbol of esteems that also there Okay even
we proved one When When When When we
filed a patent Because we failed We can
talk Maybe hopefully not the contract complete context
were once particular situation off it at him
Also betrayed because that's the basic one I
should I need to prove that why I
require an and symbol often LST um no
I didn't for some sequence The problem is
statement the same Okay but I talked about
multi step sequence Okay Without going into too
many details we try They re my realize
Able Trident for some for some sequences but
not all Whereas this one is able to
prove it So that's one example Okay So
once you have convincing then let's make it
more interactive for five more minutes You just
do I agree Okay So I'll a few
just You can type in your respective machines
or you can just follow me with this
l s d m with Okay This from
the hard work guys I'll show you one
example after that we can even you can
enjoy it based on your time When uh
well the limitation I will say is not
a computer Anything over time Whenever we help
we're in this one That uh that I
mean it in time when the ford and
stuff Right So But I I do especially
when I interacted some of the I 80
professors Right So they got I said also
doing some good job So for example I
80 Hyderabad professor was talking on a grad
camp plus plus that's also becoming popular Oh
would be such as happening because they have
the they start dared No the racing is
the started The year your data science But
of course also everything is already there on
there claiming Okay we're the first ones number
The one year courses Six months worth over
there talking on even the in us I
think cmu started And even in India also
be taken Artificial intelligence be taken data science
they're talking about Ah normal But if you
ask me what is the difference You remember
the idea I was there She s a
90 but I don't know that the least
even in really Also I think my eighties
also I remember my b tech is in
computer science and I'm taking the 90 Okay
But if you ask me what is the
difference to subject I used to tell the
difference is okay your go something subjects that's
ok but is more like because we want
to go and cash the it is there
and that puts kids are away for a
coupla For we haven't seen any be taken
block generally what at this stage Because the
pain but yeah they're protecting for next fight
And yes it will be there naturally Say
what They will be learning something on mathematics
They need to do much more Foundation instead
of the courts tests on electrical engineering another
put more on map You're able to open
this There are so many examples but I
interest rate that I can pick one of
them And I'll tell you how to use
that This is this much crazy tonight when
I start with that So what is the
background Let me give it to you What
I picked up Just Can you see this
All of your till is fine here with
me There are so many examples like this
Okay These are all different examples using the
less time Okay we can pick one off
them Okay How many have examples that one
jewel Well then 15 18 Great 12 15
Okay let's pick one of the example Maybe
the human state I want to show you
the complex 1 to 10 How it looks
like Okay if I pick on that what
is happening here on the let's understand And
you see some kind of a graph What
is your first thing What is that Sex
is what is my access Can you see
what is X axis The video also on
days when you can see it Okay I
can play it also But anyway I will
twice over because I know what they're in
that Okay I'll tell one example Then we
will enjoy Spend some more time okay And
see the video So this fight and minutes
video Okay I will give the just of
that OK The X axis is nothing but
the dimensional I mean the words Can you
see that That's what the time dimension is
because it's the world's But typically these words
are coming in extreme minus one I'm in
extra e x t plus one like that
The times What is this one He didn't
stay there Okay How The influence of that
hidden state is there now it is looking
like a crazy thing right I can't get
anything from their correct but let's sell it
something Maybe this example they talk about Let's
a little prince selects anything Okay Some something
I picked up some random thing Can you
see there is a line So it is
showing actually how the an epic of these
things Okay these two as what Have nothing
But this too Is there nothing about two
words that will be worth embedding will be
there for that Okay So what is the
influence off that in the previous one Okay
Is there any connection with this one That
means it is at least going stable right
It's not like completely sometimes what will happen
They will not be anything in this Ah
zero When something is zero means that bloggers
determines what There is nothing to remember from
this not not adding any value for this
But here it is not like that right
Maybe there is something they could doing back
But at least can you see some about
racial Also let me they have more impact
on this particular because ultimately we're trying to
learn some sequence or pattern from there somewhere
that it will be impact in the hidden
state somewhere It is not but a little
more understanding on that Maybe you can see
matching off that it will be a little
more crazy And now I study when you
do the matching off that Can you see
here some words Okay There is a match
going here Maybe part so speech also You
can add name in detail so you can
add Let me see Name So But these
I'm thinking that you understand what this parts
of speech What is any are But even
if you are some gaps is my duty
to tomorrow I'll fill it up But the
basics You know that under the these What
I'm talking about is in here because mission
What is the other one Parts of speech
known were born All right We'll just do
a quick recap tomorrow But here what is
strength hotel is whether it is a person
Oh I don't know what the GP proper
known gp and remember That's okay The problems
under coming here different colors Courts you can
see here are even if you click only
one off them That will also Sure you
What is the impact off died in particular
What we selected it What is the little
prince we selected Rate So it's not like
out of the box that what is coming
There is some context already coming up In
terms of that the importance of that these
many states are there Okay So if it
is a little crazy to understand even that
there is one more simple example also off
a structure of the numbers and all But
what is the key Take away here And
it looks so crazy like this But let
let's see What is the key Take care
for us okay No see the duck previously
the difference means what at least it has
some relation is not too crazy right At
least some state But the dominance it has
is very common one OK we haven't done
any processes Typically by the time we reach
your we remove all the stop worsened Also
that we want to I didn't fail only
the interesting patterns But let's say for for
that matter if we haven't done that now
these many times that dies Actually playing the
important role in the hidden state No the
presentation wise because there partly because they are
added Okay this is based on parts of
speech based on name and they have given
different ah funds also Okay you can see
the process also you can see all the
other There's so many places It is their
right That's right is a little crazy combination
like that match count with respect to the
particulars intense We have seen that count right
You remember if I have a sentence off
I was one of the sentence I wish
for you to have a long thing If
they have different sentences how many times it
is matching with that The scent just elected
How many times it is matching the existing
sentences So this is also if you can
buy you please take it is also optional
But I am saying optional This kind of
a tissue should understand what is the less
terminal If it is adding some value for
you please spend something I think the key
ticket days There's a lot of human states
happening in that the importance What I'm talking
now is unless you do the proper language
prosecuting it will waste a lot of like
this Typically that that need not be there
in that actually speaking if I do some
kind of pre processing already I will concentrate
only on the main important ones Okay that
also adds to accomplish that so you can
see the lines here the states of changing
because there is a What am Okay let
me think that I need to remember this
The state after that it would say Okay
No no there's not much important Let go
down And maybe there is another elements like
me Remember that that context switching is happening
so frequently And this is a couple of
it all this name entity in parts of
speech and all that stuff Okay if you're
interested in and spending time on that you
can spend some time Okay I also started
showing only for a couple of badges when
they can really take it s place Yes
So school also available if you want to
change it Yes That is also open sourced
These are somebody else's product that maybe if
you usually live a lot maybe some more
lab they open socially Okay I don't think
it's very world but at least I started
initial I don't think for Ah you guess
I haven't shown this No I haven't on
but you are The first batch of 16
hours I hate us is number 16 ounces
Still we're not covering Able to pull these
or any NATO was I can't think conduct
but at least 16 hours is a good
enough to get the good understanding Okay But
I strongly recommend I have even uh don't
complete the entire story because But I have
a good references at the end of that
OK we can discuss that tomorrow Okay So
good to Gordon Lowe Okay Thank you Nowadays
most off the modern PlayStation algorithms the packages
available right Internet is doing the cleaning job
Also made the job very simple If you
see before these are Don's dough appreciation and
talk Initiation concept was a war Initially people
are doing manually you know which packages they
are used before manually and I'll take a
toolkit Have you heard about this natural language
natural language to work it even still also
if you want to make your own customized
the processing cleaning and also people are preferring
that if you don't want to use the
general generally co processing I want toe so
very customized I want to build vocabulary One
leave for the sizes are four length are
only with the size off I character land
like that You can build your own by
using and it to get your kid You
have to do a lot off pre processing
and cleaning with your data you can do
some filtering on all those six But the
apart from that today on this boat to
work in buildings all this organization to verify
direct PlayStation it will do the basic necessary
cleaning whatever is required Playing off the stop
words come out fluctuations these things and all
but the basic things that will do beyond
that If you want to do anything especially
you can You have to depend on and
get back It just you have to do
it But regarding this rotors data already that
all the cleaning and stuff was done Only
thing is what you have to specify The
vocabulary size going to a cavalry size You
want to build your mortal so it's not
necessary that you you need all the all
the vocabulary No top words you're going to
take so all the words will be arranged
on the uh in terms off the frequency
of occurrence The most predominantly use the least
words we know we're not necessary toe included
Very rarely any document will have the count
there so it's not necessary to build you
a very big model by using although cavalry
So we have to filter out the top
words which has a predominantly used words that
is appearing in almost all the documents So
that is why what I say that maximum
words What is the maximum words you're utilizing
to the model So here we say 1000
words when he said I was annoyed will
take the top 1000 words to build a
model that means for every every article is
represented by 1000 columns right Unique 1000 columns
there are there are certain certain article will
be no not necessary that every article will
have the end Police for all 1000 columns
right Hardly like out of 1000 Maybe the
rest of an article will be feeling only
86 columns Certain article will fill 1 46
columns said an article will have 200 columns
so they are very rarely that almost practically
impossible that any article will have all the
counts for all 1000 columns That scenario happened
very that almost like in a movie like
a movie for example the movie example right
I told you like GST and politics won't
come so it don't happen like so What
kind of database News I just for it
So what kind of data Data structure you
said just for this two dimensional lists They
to the list So that's what your data
is loaded us here as a truly list
When you load this data that means every
door the let off every door is unique
You can check it 1st 1st you Lord
that didn't check Okay this is the first
record What kind of including By looking at
the valley What do you infer I don't
It is a cone base including And if
you want to check the length 87 that
means about 12,000 only 87 columns And before
the remaining will be zero Got it So
that is a maximum A community the size
you want to build for your data Said
so similarly can check the second record second
record What is the length of second record
for 36 is variable And okay so you
should understand Or data structures Your data is
loaded So why loading itself be how the
data is the extreme Oh airplane expressed witnessed
this fashion We have the data and also
the number of classes number off classes is
collected here We have for this ex classes
that is also printed here So total number
of training examples We how 8982 records of
used for the training and to do for
six for the testing Total number of classes
is 46 After that we are pausing to
binary and Cody We are doing binary including
with the organization Still if you want you
can also use with the with the count
data itself as a P if I d
also so here we are passing this extreme
and extort organizer So that organizer is defined
with the maximum number of words for 1000
words And you run this portion and you
check extreme No it will be binary and
Gordon now So I ran this portion Look
at your data size Your data sizes 8982
records on each record has 1000 columns No
this time your your frequency count is replaced
by binary values Either values present or not
one and zeros So how do you make
sure that so already we have displayed this
right ex train off one the same thing
I'm Now I'm going to display here after
binary including like this Does this those in
points So 1000 points you can see Hardly
Because only 86 locations are filled All remaining
czar zero So wherever she is one is
that that word is presenting that Okay once
the open party or day does it I
start The job is as usual You're familiar
with that The same story We're defending the
modern This model is done with auto But
I do think CNN Layer will make sense
for this data Would it make sense Study
Oh already even new medical Also we have
played this really great CNN But it is
a binary in quarter that is also hard
to think already It is very sparse already
This mattress is very sparse not necessary Ones
and zeros on Levi have records so far
Spars my tricks We generally be applied We
don't apply seeing it So this is very
spot so directly I go with this layer
Densely I start with a dense layer my
first kid inlaid with vital neurons and especially
firing input Shape your input shapers What number
off Maxwell's There's 1000 Come on This is
a way One way to specify here is
directly here You can specify evacuation Really You
another way to do that This this is
you can do like this both are equal
Equal int separate line is basically model daughter
Actuacion off Real You are here itself usually
what we do we do here Comma activation
is really rate That is one sale This
is under the state You do it like
this So this is the activation function defined
for the first kid And later Really So
here I introduce drop outlier Kerry introduced drop
player because we have lots of zeroes Facet
is more so I do some random dropping
off neurons flying fight 50% drop boat here
and again here What is this Layer awkwardly
awkwardly is designed with a number of classes
46 46 neurons already You are output classes
is ah heart including is done your way
Your labels are all the labels are represented
with 46 binary victors It's so hard including
us then So we compiled the model We
fit the model This is a procedure you
know Already compiled for it and evaluate Yes
What It all fits yet every dimensional data
you get did I mention itself is wrong
right It No After drop what we have
densely out quickly This is outwardly drop voter
dropper layer we are introducing There are different
levels of locations You can use the drop
or where the layers you can use dropout
Suppose you're using conversationally They're also after can
relation later Also you can put a drop
out even after Max for later Also you
can introduce the drop boat but here you
know to use can relation And Maxwell after
the first kid and layer I introduce her
bra port supposing here in layers how many
neurons you have 512 neurons and finally total
neurons are there and the input size also
you see 1000 The 1000 neurons is connecting
to 512 How many computing elements are there
That is all your network topology shows right
When you display the architecture of any deal
model how maney computing elements are there out
off those money computing elements You're randomly dropped
some 50% age off the neurons computing notes
you're not considered That means those north the
way to our lives You're not updating You're
leaving It is pacifists Randomly Randomly randomly Will
take For example See 50% of the waves
is not updating 50% of the weights is
not learning for that pattern What will happen
inside Let us think What will happen Study
No no Okay get understand my question Instead
Off making all the notes to learn the
weeks you're going to black back Propagate the
error and all the rates is going to
update Right You are saying that 50% off
the randomly selected Some notes You please don't
update for this pattern That means what you're
communicating to the model study Sorry Please Of
course This is a process for generalization But
literally literally what's happening and say to them
that sort only for those neurons Weights are
not updating It communicates what aspect off learning
there the remaining neurons your war loading I
make I mean remaining neurons I'm or loading
even in gaze If for people to know
the neuron sleeps you please You should rescue
this You should take cat off this route
That sort of generalisation That's the concept of
generalization right Not every neuron is learning every
patterns So you are incorporating the generalization in
this election So you're shutting off these these
many neurons You please don't update Let others
take care off the process So we warlord
the remaining neurons to take care off to
understand this pattern So So that is what
we communicate to the model as a generalization
is incorporated Yeah half off it half off
it 50% if you If you think See
that's what you should not do over generalization
Sometimes it'll underfoot Suppose you say 50% days
of the end off the day If you
get 40% accuracy means immediately What wattage of
straight You should come tactically here Maybe I
did a lot off Drop out No you
just try to reduce it itself 50% Did
you put 20% It's going toe So these
are the things Seeing the output again you
come back and where you should just go
and find units work by practice It'll come
experience and practice but I will be transmitted
only those neurons rates won't be updated That
means updated in the sense it's not learning
that pattern right Those weights are not Those
neurons are not learning that pattern only the
other other neurons is taking extra Lord to
learn that pattern What That's what the redundancy
or reducing them No Seiss You're limiting the
size because they're Spar City after Oliver disperse
then only I can confidently apply Drop over
there If my data is not spars that
it's too risky to apply Spar City there
If it is not spots basically we can
relation the result off relation layer You have
a lot of Spar City right There are
lots of zeros So that's why that also
you have scope to apply some report So
I saw the things is known to you
because it's a magical s problem So it
is a categorical crossing trophy then more befitting
the model The backside is what is the
back seats here And also this is automated
Ah validations be past This is one sale
We have seen a steady rate 10% of
the record I use it for the validation
process that saves his 32 number off Foxfire
Books is done for fire books After fire
books we got 86 88.67% And since you
have 46 records your confusion your class Confusion
Matics is 46 Cross 46 Hard to interpret
this your first record No that is the
first category I also for this is your
tested data right The zero latest You know
what Your district or each car degree Kamini
how many times it is appearing Yesterday We
did Value comes right using Malik owns that
You can find it out using Malik owns
what is account off test record under each
category for example Discard Agree we have 10
comes so out off 10 News nine news
are currently recognized So this card agree one
is a misclassification There's a one wrong classification
Maybe You see this is not visible unless
otherwise you represent all 46 46 Then only
come to know it Because is there any
miss classifications Are only does it only 10
records How do you display Okay this is
the first rule This is what's trough See
a confession metrics So you can see there
is one more misclassification here in this color
Okay that means the total recording the first
categories here Also I am 10 11 12
after after world record in this first category
nine historically classified and these other misclassification is
similarly can check for every like every law
You can check your confusion matrix and also
you can see the classifications report Also you
can see the first car degree Hominy records
12 records right The first category zero We
have 12 records out off toe Only nine
is qari classifications So it depends Your recall
and precision score for every records Almost the
same thing only are going to do with
the movie data assignment Your movie data statement
the same thing you're going to do Only
differences work in snow 46 classes You'll be
doing only two class That's the difference This
is multi class problem in our movie data
A statement you are doing with only how
toe master to rose will come zero and
one Only two types of sentiment cost you
a negative Total number of record not correct
records Total number of records in your test
record Festivity Oh Toft was No Zero is
the one category off News zero label is
one category of loose study 12 year year
12 12 out of 12 only nine are
correctly recognized right And if you are split
and no some of the guy dignity Which
one Yeah Yes sir In 200 everything you
multiply with 100% age All are represented in
percentage This is comp This is going and
Worrell averages given in the bottom for all
the card again Yeah Yeah Uh study Uh
yeah Correct Correct violation Smith Yesterday we used
right 10% of the record is used for
the renovation You see the evaluation during their
book the book You see the harmony the
courts are used It is It is taking
only training 8083 samples and validate on 8
99 samples There's 8 99 sample is what
Your test record Although these 8 99 records
you just if you total all these things
that will come toe 8 99 records if
you sum all this No this is some
off All these things will boils down to
8 99 records No again I have to
teach yesterday Topic don't compare with the Emel
Methodology and deal methodology are different in the
MLB How model dot foot separate mortal dot
Predict is different but you're saying function You
do that Laney has elasticity I don't think
his work to the training itself for two
in one It's training process Also carried out
with this much percentage of the test records
Both are done in same line Where is
your mission Learning separately You do model not
fit And then model not predict you're doing
it separately The talk This is a different
style of al evaluation No we can also
use that sample You know if you don't
pass it it will take that The x
test That sort If you see the see
wonders manual another's ultimate Anyhow I didn't do
it purposefully I didn't You strange dysfunction If
you notice I didn't do strange dysfunction by
default Kiddos data when you read the kiddos
data set building data said this is the
way you have to collect it You have
to collect it Four relievers extremely expressway train
way test I didn't manually applied scientists ritual
Yeah the tour This is a percentage is
not in my control Right What are the
building Data said home Hope How they split
is not in my control Suppose if I
want to evaluate for 60 40 70 30
otherwise after depend on the building Data said
this is how they respected the data If
you want to go with your one person
data means this is the only way you
have to do positive validation split Otherwise you
harbor depend upon one standard This is how
they report that they say 50,000 data for
the training and 10,000 data for the testing
That is how they build the building data
When the Lord building data your data will
be slated like 55 10 But if you
want to test it with your 1% where
is the school You should have scope right
I should not depend only on the building
data How dispirited 50,000 10,000 If I want
to make it 20,500 tested You should have
an option That option is what validation Spread
the school You're passing it No Same thing
I printed twice Same thing is printed Toys
No time Spender score on liberated So we
have to give ah model or develop eight
Extreme Why train to see the the accuracy
for training their daughter That's what we have
to do it Okay Instead of extensive writers
who should give like extreme my plane See
circulate That's just called inaccuracy both of seeing
After all I'm printing the same variable rate
school school Actually should circulation be scored off
Zero Score off one score of the Revolution
restored the loss and score off One will
have that Chris but he had to spend
that Both are same variable speed That's why
one of the other is that what are
the values stores score Will store score will
store to Allah's honors loss Okay One of
the last function that is Ah lost value
Relegation loss Another is that I couldn't see
So it's you can you can put score
of zero and score off warning and you
can see when we're loading the data The
first step So there were mentioned this political
the point too right So it means in
the train only the remaining 80% that I
will come So my question is because this
20% is a new you're not using it
So I do a viewpoint we can give
you going to another list Then that and
then in the violation stuff you can use
the more records for training Is it right
I'm asking Is that the right way off
Thinking isn't in the right direction You said
that is the baby Ah load the data
into training test So or do we give
that split there Why not We take the
entire data and then in the violation step
we all anywhere getting giving this protest point
Yeah There's no way connection between that person
agent this person did this person ages independent
off that one This is 10% a job
you are Training sample is utilized before the
validation It is taking the 10% age off
your training samples for the validation I say
no 10% age for the distinct 90% dates
from the training See otherwise See if you
have this confusion better you give like this
You have to specify like this If you
want to use the weight when you're loading
the data itself you specify the percentage right
20% of the total percentage that data that
percentage itself If you want to use it
for the validation here also battery especially forever
in relation to score data quality express twice
Just it is It will use the same
person pitch Well yeah yeah here This one
we have removed this Only this one should
be them order That's why in primal we
have their two things No wonders with so
manual validation This is what Manimal procedure And
if you don't If you don't want to
use that value this is auto verification for
auto verification We have to specify like this
violation spring That means we are We are
not using that We're loading the radar Whatever
the person did you collected No that that
one I'm not utilizing it That is a
meaning I'm specifying the percentage here If you
want to utilize that you have to say
your validation data within pupil Hard to pass
expressed rightist to procedures of each other's you
want You can do either No that is
a separate function Is there 40 If I
do a crazy So we have to use
that The function itself different Not simple To
organizer the function itself We have to use
a different function called P If I do
a crazy that you have been important you
have to use that function Be afraid Eric
places So that s close This so nothing
new We are doing it here in this
Great All right Except we have Preparation is
different right Preparing the data is a binary
encoded data Shall we proceed further They know
there's no point in spending more time on
this Okay First you tell me the few
applications are problem or domain area where we
we have the scope off sequence prediction Maybe
one thing you can tell from this diagram
what do you can tell What do you
infer from this I didn't ask you that
I'll go to them how or in in
is going to work And I'm just asking
the application first We'll start from the applications
must understand the application Then we'll come to
the algorithm The feedback network covered some All
the story will discuss So this block is
present in the moment You look at this
block What kind of obligations Liking River Stockwell
Prediction There's in the last five alleys Tomorrow's
what would be the stock value tomorrow I
can do the prediction Right Weather forecasting with
it based on the last flight is a
month off millimeter offering how much of this
rain I can predict tomorrow's rain So this
is first model is many to one more
than great this time Cities not similar time
series this time cities sequence instead of time
Cities were calling it sequence sequence Sometimes it
is water saying sequence Why I call the
common name sequence Tell me the reason why
it is not particularly specifying time series Other
improvements No we're not understanding my question I
think for example you understand my question What
is no not named Best time citizen That's
what he asked Why does not name this
time situs off course A logically this time
Something like a time cities We can't solve
time See this problem Also one of the
problem we can solve But not only the
time Serious problem is not solved by your
out An analyst Yeah no date Day to
day life We have a lot off obligation
while you mobile phone your text Next world
it's predicts rate is it Is it based
on time based It's not based on time
based Your next word Your mobile phone Your
text editor is predicting right that problem are
so it is here Your model is solving
that problem or so do you call That
is the time Cities forecasting There's no time
stamping here but just one leaf 5% sequence
off letters Uh the sequence I trained the
model to remember the sequence So what are
the combinations you want to make them order
to remember what other after Jack What should
come after Jill What should come You have
all the permutation and combinations If you have
the combinations off these words sequences and you
train the model your model is going to
say after this word you're supposed to have
this work They resort in the text when
you type No the next word based on
your past inputs So all the detail analysis
we see how do how it predicts and
all We didn't go to the algorithm for
timing At least you identify the problem which
are the potential problem which is going to
come under armed record a neural network that
is else LST um is this likely to
make a diversion off for dinner We don't
use directly ordinary with a slightly to Croatian
off foreign in the sense we are going
to increase a little bit a capability more
Capital Dion ordinance that is popularly called this
a list you long short term memory The
meaning is long short term memory way This
cold is long short term memory and all
you'll slowly shortly understand once you understood the
logic behind architecture Okay so there are two
problems were addressed here on those numbers that
they say 15 26 46 and are for
the study too This is one kind of
problem If you have any medical values your
model is going to predict the next numerical
value Jack Jack and Jim do we solve
this kind of problem Shall we solve this
kind of problem Right That is another model
right We can also build a mortar to
predict the next word After Jill if you
are playing the Jill and went is one
combination Okay Any other problem or any other
areas and you can suggest sequence tradition What
is your assignment or your assignment You want
to predict your movie review s post your
negatives A sentimental analysis Shall we apply that
problem here Will it that fit into here
Is it possible what you are going to
do This is a sequence you present to
the mortal and you're making the model to
remember this is one label This is a
sequence you're presenting to the mortal and you're
making the mortar to remember This is another
one What is the difference between this and
this Tell me using See You're only where
we are talking about sequence prediction Here it
is predicting the next word Here it is
predicting your sequence belongs to which class That's
the difference It's going to predict my next
word off my sequence This is one kind
of problem here Your modern is going to
us Sequence belongs to which category so it
is called sequence classifications Problem This is sequence
prediction program We can also use sequence classification
model You can build the LSD a model
that is orange and model even for movie
review date Also sentimental analysis Also you get
up like what is the difference by using
this model and using that model What is
the difference Primarily this super busy little technical
and not asking because I'm not introduced architecture
as a superficially as a bird's eye view
as a abstract What is the fundamental difference
Do you think you develop a deep learning
model You recognize the things and all you
apply to the end and architecture You make
the model to remember one and make the
model remember zero on a number of classes
here Also you can trying for a number
off Don't think it's what we live for
Vanity You can do it for a number
off glasses even the news group date Also
you can apply this model but it use
a different logic That's what I'm asking What
logic it picks in deep learning model in
the planning model Or do you have solved
just before each word is represented numerically or
some other country Israel value are binary valley
whatever it is Simply it remembers that as
our independent features that's what they're remembering This
is independent features but it does not explore
the context across the across the feature Is
there any context across the feature Dudley Independently
This fight independently This is for you present
as a neuron Make the model toe just
like any other No Like a primary Indians
What is it about The difference imply mind
Anyhow feature is no that we are numbers
Nothing new The same numbers I'm making them
order to learn this These are the numbers
you remember a zero If these are the
number he remembers one I didn't explore this
This relationship the context before this what word
was before that What we're doing I need
your water is going to explore that sequence
She'll context Don't go into the picture Do
you think Sally able to predict what last
it belongs to That is a capability or
or in in our celestial model is going
to bring okay Any other any other area
application Do you think off that is algorithm
will come to their golden How it'll work
It will take feedback I didn't come to
the garden Please hold on I'll come to
a good moment if I showed Al Gore
limit immediately Losing Chris I'm damn sure See
if you are interested Feedback Feedback You want
to see feedback See this one will scare
you Are you see I can dark it
So this is ordinance This is auto feedback
Network is goes on like this What do
understand most context I'm bringing the context where
you could apply this How the feedbacks happening
and all will come to this I come
to this Please wait Hold on any other
context Tell me any other context where we
use it I think Roddick Recommendation Suppose you
are purchasing in a maison Okay Last year
This Monday purchase this product and this product
they can Reddick What could be the next
product you're supposed to purchase You have a
sequence Right So last year your party is
camera on the next product Europa cheese the
laptop then mobile Then your for example Probably
Then sometimes this to predict from the past
pattern reasonably not 100% each But reasonably you
can use off the LSD in order to
predict what would be the next product You
are supposed to purchase Genesis Oh Uh huh
Yeah If you have the data and patterns
available Yeah we can predict the volume of
traffic Also study Well this is also another
way of doing it better And all off
the hand I cannot And I have to
cook and see the taste before cooking I
cannot I suppose if I did the CPI
for you need a super If I see
Shall we prepare the same recipe Micro own
Also I can say Yeah it's possible Will
it taste better I have to prepare and
see Howard based then only you can see
Understood Late Let me see There Plenty off
options Are there same problem Different approaches Right
now you're slowly uncovering right Almost everything is
possible at any model but depends upon how
do you How do you use that data
for that model Previously told movie sentiment analysis
You You thought one Leave it CNN are
deep learning model only it'll be solvable now
the moment I gave the context Now you
agree that even sentimental analysis for the movie
we did you and you can use a
list immortal That understanding should come first What
to do for what kind of problem If
that understanding comes how to do is a
man property how could anyone can do Because
the provider technology and tools algorithm libraries that
there it's a matter offorty All this example
we discussed right Okay What kind of problem
this will fit into For example Chad books
What kind of problem Chad boxing treating toe
You passed some quickie That is also sequence
off later Right sequence off words What other
courses do you offer Bill You offer So
it was you opening the Great Legs website
on the middle Check Chad box pop ups
Suppose you're typing it What kind of problem
do you can make this problem here to
this context or not If we connect what
How it will be different from these These
examples Howard is different from these examples He
carefully analyzed all these examples carefully and tell
me No that is the way off for
implementation everything that this also I can make
it interactive So go to some website Some
review of your type which interactive you can
type The review rate yesterday saw the movie
This movie was so so so so and
so and click it they don't say the
answer right That's the way off programming just
all so I can make us a continuous
process OK do you The next two movie
review I'll give the scoring and rating that
is a way off implementation strategy I'm asking
about the logically how it is different this
and no one fundamental difference I'm asking Don't
think more too complex very fundamental question In
what way This kind of problem different from
here it's not multi class It's not meticulous
again You are making mistakes This is many
to many mapping input Also sequence off words
Output Also not single word It's the sequence
offers You got the point Include also sequence
off words Awkward is not If it is
a classification mortal like this your output is
going to say Okay this is this recorder
This is a recent sentiment Another wonders What
if this is a sequence offered It is
going to predict one next immediate work again
Many do One If this is a sequence
off words I put ourselves The sequence offers
its many too many sequence mapping Got the
difference Okay this comes under many too many
mapping Your wife's translation also comes into the
the speech Translation So you have a lot
of options to choose it How do you
design the morning It's a 1 to 1
mapping or one to many mapping or many
to one mapping you have plenty of options
are available You have to find out your
application fit into what kind of model it
is Suppose you say something and you're immediately
see uh suppose you speak in English I
knew response is going to say in your
local language that is also many too many
day So it's on Orleans off TexUtil It
will convert into wise and splaying back That's
again It is a very popular product Google
is developing right Suppose you land up a
China China you don't land sleep Mission translation
is an example of mission translation chat box
as well as mission translation Whatever the question
you speak in English This translator will be
always your hearing that will understand as input
many sequins off words as input It's going
to throw the output Also sequence of words
and Chinese words So this application is mission
plants Elation application mission translations one language to
another language conversion Are Chad books English to
English Whatever the language Whatever the language how
do you map on more than basically everything
At the end of the day it boils
down to the numbers brother That's these texts
How do you map into numbers and this
numbers hold you map do the output How
do you map then put a note Any
other example or application Do you think off
Okay this is what the sequence classifications Problem
sequence classification problem Suppose you have the sequence
off these numbers and I think but in
quarter your sequence of words is numerically included
That is a movie review Our hotel review
whatever This some numerical Ian Corder And if
you if I pass the sequins off words
my model is going to say it is
a good or bad So this is a
sequence classification mortal something like this again here
there is no limit off number of classes
Don't think only binary you can use multi
class also So not only sentimental analysis you
can think off other technology other dominos So
for example the in a sequence classifications if
a given ready any sequence off SCG devalues
Predict whether the sequence for quoting or non
courting So you have plenty off applications and
choices of the under this category Yeah this
is all the application I talking about this
many to many mapping mission translation rather than
predicting a single next value in the sequence
Okay a new sequences predicted that may or
may not have the same length That is
an important point to catch you not necessarily
to be the same land when you are
asking the question in the chat box not
necessarily or Chad Bach will answer with the
same equal number off length So these are
the challenges in which you cannot use conventional
new network in conventional urinate All of you
have followed the fixer number off inputs I
fix a number of hold put right but
it's going to dynamically change Sometimes you're in
Porto half 10 sequins sometimes vilify sequence So
these are some of the practical challenges That
is why it is emerges A separate area
called recurrent neural network is going to solve
this problem more effectively The talk this is
a challenge is that's what I'm talking about
the conventional more of these limitations off multilayered
perceptions even a classical uh neural network model
If you want to apply the important point
here is water The dimensionality of the inputs
and output is known and fix it because
of these reasons We don't use a classical
multi layered Perceptron model here but we can
use ah deep learning modern deep learning model
It's possible study Okay Yeah deep learning means
Ah don't say a deep learning mean CNN
CNN is apart off deplaning It's depends upon
whether you use it or not Classical neural
network I'm talking about the classical neural network
is a limitation of classical but deal You
can sort because you're going to solve your
I think the movie review data set you
have two options with you that isn't a
segment Ask for you right I don't know
All off you're going to solve the same
approach or not But I could recommend you
if you have a split in batches at
least one batch You tryin deep learning model
Another batch You could try in a less
team order That could be a good strategy
to compare otherwise Everyone You approaching single problem
No you don't have an opportunity to see
the result off the other approaches But this
is just my personal thought You please check
with program office and I finally don't blame
my child Only he told like this like
that Whatever the sections you get from programmer
please please follow that This is my personal
opinion I came to our favorite topic Okay
This is work they are looking for right
from the beginning What In the normal neural
network I'm talking about the classical unit or
or feed forward during the back propagation Multi
layer perceptron back propagation Neural network is a
feet forward or feedback network feedback Oh feet
forward Our deep learning model is a feet
forward or feedback network It is a feed
forward network You a classical neural network is
a feed forward That is MLP classifier which
is available in a scaler I think you're
not Use this have used this This is
a one liner You can just easy look
like Suppose you say model equal into Imagine
if you want to apply this for the
but I my Indians data moral equal and
doing a multi classifier hidden underscore layers only
This is our money or to pass when
they say eight Come I ate That's up
What It means that eight come I ate
Means used to hit and layers a layer
one hidden layer to each would have eight
neurons 345678 12345678 We know what is the
input for prime I are distressed Data What
data We have seen um housing values study
You can also try OK but that is
a regression problem Okay primary data Eight neurons
rate eight Otherwise you'll get confused between the
sake Okay let me take something like 10
comma uh six What it means your input
layer will be eight neurons 345678 and hidden
layer one should have 10 neurons and had
a layer to should have 34566 mirrors This
an architecture and output will be one So
how do you do Mortal outfit Mortal doctrine
The X ray wiping Okay then why I
predict equal into mortal door Predict the Ex
test Compare your white predicted waiter's job in
same neural network model is available in psychic
learned library in the name off in the
face off mission Learning There we don't call
it as a deep learning is a classical
noodle network model is so shallow even also
you can increase the model you can make
This model is a deeper there's or restriction
here Instead you say that for example 10
come off income are going to come off
article Maybe still it'll work when you're or
loading it but to certain extent only work
one point of time It'll hang because it
does not have the architecture to handle it
Multi layered perceptron not sallow That's what I
say No if you give like this is
not shallow It's a deeper still You can
apply but does not Has a parallel sophisticated
architecture something like a 10 cell phone cameras
that is also new network I can call
it as a smaller network smaller network smaller
neural network What you want See you be
used a deep learning only if you do
it in a 13 or tensorflow That's it
Otherwise don't use the word deep learning if
anything with this implemented in tensorflow battle architecture
but get us back east That model alone
it's called is a deep learning So all
other model we simply say Neural network model
that sort simply neural network model you can
make in different name like small little network
Shorter neural network are shallow neural net or
whatever the name you want to give you
can give so deep learning we are focusing
on We have a sophisticated platform Computation follow
Your tensorflow is giving the computational power to
do this Otherwise with MLP classified you cannot
think off are playing the routers Data are
the Yemeni STD Does it 50 Those example
40,000 samples cannot You cannot think off That
is simple This morning with this model you
cannot think off even beyond the fighters and
records itself It'll hangar 1000 or 10,000 records
this system will hang That is why we
have a sophisticated architecture to handle the large
data sets So you are It is a
framework for the better framework for the new
network model That's it But in what machine
Learning euro Your classical You're on a dark
and deep learning have the same strategy off
updating the waves There's no difference It's the
back propagation Only the other is propagated back
to update the weeks understood We call this
a stochastic gradient descent but off course then
deep learning model There's like improvement in terms
off on after woman concept also incorporated So
that is still increasing the efficiency off their
performance convergence You have very good convergence but
the classical model has ah sometimes it will
take a lot of time to converge You
have to run it for one overtone can
watch converge in the sense of what you
read Your last will be a point for
10.0.5 It'll never come below so you get
only 40% of peoples and accuracy because it
swallows up such a city Whereas here it
follows that happy moment So you are read
Adaptation is saying as your hair is propagating
back Okay Yeah You can you I didn't
pass the other but I mean does it
take the default parameters that before they focus
200 You don't think otherwise You can specify
He pokes equal and you can give Everything
is available so the default learning rate also
available You can If you want to give
your one learning red you can specify a
lot a logical and to so and so
Let's say if you want to give 0.1
fight you can specify learning right Your books
If you want to specify their books it
books became specified Everything This was a what
We do it anodyne in what we do
in our inner Additionally the percent import this
input will be pausing onto this way Okay
and you have the output This awkward is
nothing but a summation off X include extreme
to w right and I'll quote you get
extend the w off course activation function is
active here You have your fourth net as
out for training Local Additionally what we do
This output has taken to the input like
a neural network That means what When you're
presenting your second record this is what Your
sample One sample one sample Wonder how present
there And your it is your modernists shown
some output Okay for the sample one Let
us a point fight when you're presenting sample
to in your present example to along with
your sample to is one input along with
that your previous all could also come as
another input You're on your own That's the
difference Just simply the output I didn't calculate
any other and all I didn't help it
any other way It's not simply the output
Some of the excess fight Suppose the excess
fight and your weight this point fight They
dis wind fight on my biases Let us
a 0.1 What is output off Neural network
five in 2.5 plus 0.1 Okay this is
two point ah to find six And this
to find six will be one levy power
minus 2.6 sig model and you get some
value between 0 to 1 You got that
output That is awkward of the neuron Director
you take that or put and feed as
they input to the each individually Right If
it is a one neuron let us talk
about one single new same process will be
applicable Toe Suppose you can have a stack
Multiple neurons Also we're talking about one single
neuron What is a process that happens in
one single neuron What you want I didn't
calculate Delta William Unless otherwise the calculator then
only Delta will come into the picture simply
the output I'll put again your feedback to
the input immediate Previous meted previous only there
Okay So instead of looking the model something
like this it will be We can flat
on this like a conventional feed forward Look
at this Whatever I told here this it
is a represented KFC Or does he want
ready access the excess travelling Imagine how only
wanted in Europe Let's talk about one neuron
Your excess travelling with the wheat W w
is the weight which connects the extra hatchery
input decadent So I called this w X
Hutch and this is a way connecting here
and talk Would y wh y which is
connecting shouldn't to open So it is called
this w hatchery So my input is traveling
to this neuron and I have the output
This or put this output is fed back
as input to the this new on again
So now I'm going toe unfold This if
you're a friend for this and every present
this if the same output is fed back
as an input Next question is what What
was that weight value for this Tell me
if this output this taken and fed back
to this neuron any input should has to
travel by our weight Right What is the
rate for this feedback study My old quarters
Let us say let us say this output
of something like 0.4 fight Okay I agree
Them 24 5 is going to multiply with
which you Wait I'm asking Huh Same week
Sam Wait Same way Wherever you are supplied
One input Okay You're supplied on input I
knew how it got out That output the
spread back and it has to close to
the same week till the time I update
the week So far I'm not update the
which it is going to pass on to
the same rate rate shall interpreted the same
sense in that sense is that clear for
you Output has to fight back to the
input means again It has to pass through
the same great still my weight is not
updated No Yeah that's what that's what we
are going toe If you can easily understand
from this dagger when unfold this Okay this
is what your current time instance sample The
xfx team means this is my current sample
at this time Instant Let us assume that
this is sample number two Correct This is
a sample number two That means you have
already present example one when you're present Example
One sample one Oh your X ray is
traveling here What is the additional input coming
to this neuron The awkward off the previous
time Instantly minus one That means when whatever
the sample you presented that b minus one
instant That means example One my example One
output This is my sample One output right
Example One hour porters is coming as another
additional input to the neuron Look at the
rates W x h w exits It's going
to travel I have the same weeks right
It's going to have the same weeks So
you are previous Instant output is coming as
a important to the additional input to the
model So something and this model output is
going This output is going as what Input
to the next layer That is next to
time Instant input in all these incentives in
order is my white It is remain saying
my weight's remains thing Okay For the first
example there was no feedback Okay let us
I started the Exhausted So how do you
write the equation for X softy Exactly how
Charlotte eight four in two point Well it
is Azuma There's no bias for simplicity I'm
not having anybody's So you have to write
This is the late late No I didn't
include that I didn't include it So this
is output So if off this value let
us assume this output is Ah 0.35 Okay
This is that No this current time Instead
this multiply with this week right No I
didn't know First I'm getting the good 1
ft forward The neuron X except current time
Instant is going to 0.5 first I need
to get the food right That output I
should take it for the next sample That
is as you import the first sample There's
no feedback because there is no This is
the first sample I'm presenting So there is
no feedback So then X softy plus one
goes as input This time same 0.35 will
come by out the same which is going
to come by at the same rich So
0.35 in 2.5 Bless Plessy This next Temple
Astri three in 2.5 So both will add
Okay both with that And you have some
value that value be paused onto the activation
function and you have the output Okay again
This output will be taken for the next
sample and it'll be going like this Libya
It is this study That's what we're going
to see This is one single neuron You
can have any number off neurons No this
is not my input I suppose you have
input and number off inputs Input Let's suppose
you're include layer That's what I'm talking about
So this is what this if it is
a normal neuron what What do you do
if it is a normal This is what
my hidden layer one This importers coming here
with the reach and you get this output
This will be propagating here where additionally What
I have I feedback every night Love you
feedback everything you feedback next layer Also this
neuron also feedback every year on your feedback
Same neuron Don't confuse it The layers here
every neuron has is this activity Every neuron
will have this activity The output will be
fed back to the same neuron The previous
input Whatever the previous in Porto your neuron
has produced for that output it produce It
is taking that also into consideration That means
is going to act as your previous input
as additional information from memory correct is acting
as a memory unit So we call this
as a memory unit every neuron It's not
only working on with the current sample it
also taking into picture the community some off
the past sample Also as a immediate past
memory there will be a propagating their list
propagating back No So finally you see no
whatever for this particular up sample you have
one output right Finally that output you compare
it the target That difference is what he
had a that there are You will be
propagating back Just someone No number I didn't
calculate So this is a simple ordinance This
an architectural simple argument But there is a
drawback off this I suppose if you are
increasing the layers if the increasing the layers
the same metal back propagation rule also applicable
here only additional feature Every neuron has its
own feedback on current additional displace off This
memory unit is coming into picture So that
means you were immediate past only or giving
feedback Right So that is why we called
us us short memory short term memory But
why we got the word long again will
come back later Short term memory What is
maybe shorter memory because you're every neuron is
not only taking the percent sample Also the
immediate bus immediate bust So just short memory
Shelagh Natives the shark memory Your ordinary will
be working very well for a small sequences
It can do a prediction Very well Do
the prediction for the small sequence If it
is a large sequence know if you're going
to give a large sequins your argument will
fail Why Because what will happen We have
something concept something like a vanish ingredient concept
of vanishing reading orders What is that I'm
telling you Suppose when you calculate the delta
air value here right every del tired value
calculate and they're really something like this 0.5
And how do you propagate back This entire
has to be propagated by the way to
Alice Right So every layer this delta value
all these infractions and you keep on multiple
as you see the delta value here on
Delta value Here let us say Delta hidden
to this that the docket and one that
is as you ritual that will be smaller
which will be greater Tell me numbers one
or two which is smaller one of the
smaller We agree this OK as I propagate
back too much Okay that means I'm approaching
towards my include lier What will happen This
will be contributing very less for the weight
adjustment a minute So you are Delta Valley
will be very good weight adjustment for the
layer which is close to the outwardly but
it will give you a very poor weight
adjustment for the layer which is attached to
the input site That is what we call
this a vanish ingredient Many think it anyway
because it's a vanishing breed it because your
data is what the greedy in factor It's
almost slow small value against small really small
really At one point of time it'll vanish
That means that depends upon that depends upon
number of Italy Generally generally used a stack
of real estate immortal Not one single A
we'll be using stacked That is why we
use our deal models right Deal or no
deal model we don't use it for singlets
will be using it for the multiple lace
when we do it when we expand that
the depth off the model This is a
problem and you're or in and has a
short memory And imagine a long sequence is
going to come sometimes when we watch some
kind to learn door Okay um according to
the incidents who is this suspect Lee Keep
thinking right Okay This will be the person
You'll have some some person in the mind
As the story progresses some other incident will
happen And you Are you a suspect You
will disappear You'll forget that person because the
context has changed the contacts associates No you
you keep this is the person will be
the suspect Okay As the movie progresses as
the sequences progresses I'm tend to forget the
very initial scenes or evens on I'm tend
to remember the recent What about the What
is the reason Context So that only I'm
tryingto update That's what that happened in our
biological ordinance our biological model So the context
is very important What the ordinary will be
suffer from Oregon is suffering from the for
the long sequence context It is not retaining
the context off long sequence on how that
is welcomed by LSE immortal unless your model
simply we don't give the sweet back simply
we have some scaling factor and filtering factor
for the feedback in the form of forgetting
getting structures That means there are certain certain
weights Okay there are certain neurons in which
the baby has to be given more that
is there something like a compensation for the
vanishing radiant because anyhow the vanishing getting there
is not going to give you enough update
on those weeks What this scaling factors are
going to do the scaling factors going to
do some boost if that But if it
feels that neuron is giving some influence on
the context If it is the important means
If we want to retain that memory means
it will give high scaling factor It does
not want to read a midget Give the
less scaling factor that is depicted by this
next Dagenham This is what this is an
extension off foreign and model This is Ah
just a little mortification off foreign model We
don't simply give the feedback Give the feedback
with some scaling So how you're going to
give the feedback with scaling That's what we're
going to see here We have three important
gates Three gates You're going to see input
Get forget gate and opened it Input Forget
and output gets I look at the flow
by looking at the flu Can you see
these three Cross 123 These are the three
gates The first one is what these are
Some of the people's I have given it
to the models Some extra people's dont simply
feedback I'm going to do something some portion
I want more Some portion I want less
imagines this is something like a block off
the pipeline Just getting some waters Okay so
you have some control mechanisms So here I
want more flow here I want less flow
here I want more for something like this
This is going to act as a filters
So what We call this as this stopping
unit As for good Gate this is the
input gate and this is output Get three
gates We are defending here and see what
other inputs coming to the forget gate Look
at the floor and tell me what other
important that comes your current input Right Your
current input on your X T off Minus
on Output off Extra of minus one means
your past Oakwood you're passed out for the
scummy Your present input This is what Your
signal activation function You get a something Something
point right You get something Point This is
the thing Mortal activation function You apply signal
activation function and this is going toe this
gate and the based on the put off
signal activation function That means you're because you're
sick Mortal activation function will fluctuate between 0
to 1 Okay Suppose if the output is
close to one Okay That means one of
the information it receives from this layer open
That means your previous or put the same
old food is coming here also as a
scaling factor Not only here I'm I'm tapping
it Care also toe this unit also your
previous unit or could also coming here That
is going to be multiplied with what This
output off this immortal activation function That means
based on the output off this it is
going to design this A weighting factor right
This is a weighting factor for previous output
Whether I should give more importance to the
previous or could at least important if the
signal output has high value means it will
give more weighting factor right That means it
will remember this more if it has to
forget this this awkward valley will be smaller
number So that is why this is this
gate is called is a for good Get
this ass to design how much it has
to remember and how much does to forget
depends upon this open Is this for good
Get And next comes This is the include
it what other things comes to your input
Get your current input Also your current your
previous or could also coming in this floor
So you are your current input This mapped
onto your hyperbolic tangent scratching function That means
your current and put the scale to two
plus two minus one It will be mapped
onto plus two minus one and that will
be multiplied with again Whatever the signal activation
from two new personal summation off Excite And
that this import this product No these two
You have someone You are play activation function
again You have an activation function here This
output along with this your importers that passed
onto this directly activation function We told multiplying
with this these two are products multiplied here
at the staff function It will be added
in this or put off this It will
be added What is the significance off this
You're forget Gate is giving some information about
your past record How much importance has to
give in for the past sample along with
that What is the importance off my current
information How much my current information is important
That contribution is given by this this unit
That contribution is giving along with that one
more output One more gate we take it
directly or this product is coming toe signal
activation function It is going to hear this
Allport I'm taking passing on to this activation
function But this is a strategy they have
designed for the strategy they have used They
split up It's tough taking one simple output
has given us a feedback They have kept
three tapping units and they have mathematically is
formulated Is that all kind of an algorithm
Right People are done with a lot of
experimentation Whether they need a scaling factor Here
are hyperbolic function Here are hyperbolic function is
here they have done some experimentations some R
and D based on out off that are
in the only We have this model There's
an architecture This is standard architecture We are
employing this architecture for our applications Why it
is here and why it is not here
and all It is a design We have
class the people who develop this We are
going to use this We are going to
be India's it off it But at least
we should understand the context off Why this
three gifts The contribution off these three gets
You're probably This gate is going to give
the influence off your pastor Immediate sample contribution
of the past sample and the current information
and cumulated lead community Really this third unit
is nothing but the cumulatively you are Forget
gate and your input information is leaving you
Are this memory said so here in the
last year model at every neuron We don't
use the term neuron and stuff using the
term neuron We'll be using the time called
Memory said but literally the same only what
it is A memory cell because it has
some tapping units here toe designed to control
the inputs at several stages This is what
we discussed the in ah Lestienne gates So
one is for good Gate decides what information
or discard from the same except maybe it
has to decide whether to discard I have
to remember it and your input Get designs
which valleys from the importance of data memory
state and your outward get is accumulation of
both the sites water or put based on
the important memory said each and every gate
Yeah yeah weights will altered only during your
little back propagation during the training process We
just discussing the forward flow on legal discussing
This is well how the information is going
forward in the information and the importers coming
or the other computations is carried out There's
no weight of patients they are not talking
about here There's a weight updates Look at
that model Actually this is north or C
This is no data back propagation I don't
think this off course rates are scaled here
Of course rates are skilled Not because off
every back propagation algorithm that will happen That
will happen I spent your hair back propagation
I'll go them But at least you have
to get you one feed forward flow right
for one feed forward flow under the competition
that happens for your current input A new
present What is the contribution or fewer Prop
Blast input And what is that Your memory
cell is throwing doubt Which and I will
be taking this report to the next two
X off people is one data So you're
presenting your data and this is what the
floor that happens into the my medicine Same
thing that happens All the nuance right This
is what was magnified This is magnified and
given same thing se memory tell you how
every neuron will have the same logic You
bring your own will have this This is
just one you don't One minute he said
this is one memory Sell these two things
you have tow Assume virtually it is not
available here You have to assume actually because
your past record you cannot see what does
that put your passport But only thing is
what Your pastor through the sweet back when
you can realize similarly your next tour next
memory cell Unless otherwise you freed the next
input you could realize it Actually these two
should be You should imagine we actually but
this is what your current state off the
scent This is the current state of the
city all new ones itself For my medicine
Don't separate neuron and memory cell different your
neuron itself actors and memories in this is
a magnified washing off your every neurons So
this output off this new Ron Howard has
decided this output off this near on is
nothing but this output right This old ah
that is based on the memory said it's
not going to be fix it That is
simple ordinance This is simple ordinance because if
you use a fixer dough value means that
there is no memory shot them so that
that that drawback is overcome to by scaling
your point for your different instant I'm not
using point where alone there I'm scaling at
I'm applying sig model function here You scale
it here and you scale it a different
way You scale it different three different ways
You scale it and you take the output
not as a simple outward as it is
like this Simply question so little complex or
put the combination of three things is coming
into the picture Is that clear Not as
a fixed point Right That 0.0.5 is scaled
by certain parameters Sick mortal activation function and
some places it is hyperbolic tangent functions are
applied That is a mathematics behind up That
is how they devise that architectures Yeah that's
what I come into that by using such
I think what happens is what you're shorter
memories or come That means your output does
not only depend upon immediate past because imagine
Ah you your This is our first to
seek once record one I called one of
the first word your you're going through You're
coming to the 1000 record right Even if
the 1000th record when you're working on that
place will be that the context will be
remembered because we're not using if accepted Wait
So that is what What is the meaning
is it is a short charter member in
the sense what I'm giving Still I'm feeling
I'm giving feedback to the immediate part sample
Only a short characteristic is retained its retained
for a longer duration off time even for
the longer sequences that means the sea Uh
yeah See when you take text is a
very good example I can tell you when
your mobile when you text messages and all
OK after a certain words after a certain
word you may be used Another word that
you might have used one month before right
You might have use one month before you
went today In between you have changed the
context when you change the context initially to
confuse your model confused 14 days before you
have used this word after this word But
today you are using this word that means
this also you taken into court like that
means that the memory right Okay your past
14 days before also after this world You
have you some other words that waited Also
come It'll a content You're more than we
cannot see It will physically but in terms
off accumulated summing points that contribution is added
somewhere So it is incorporated not only your
recent uh whatever the words you used after
a particular word It also remembers your one
month before also whatever the words you used
it I'm in case If you are using
this worse over a period of time you
never use that word right Slowly That way
digital start for good It'll forget your memory
a little for good hereafter It'll remember you
a recent things only So the meaning is
what it will It will try to remember
for the longer duration off time that is
order School is a long and short term
memories What based on your immediate stuff the
past sample were feeding back the retains Based
on we change based on once you presented
the your input and you suppose or what
us You're validating your model right So how
your model is going to learn based on
the output It compares the targets and the
same barrel back propagation algorithm was works here
updating the wheat When it's rate is updated
I'm the next input is presented And then
when the next input is presented and this
process will happen it along with your other
back propagation weight adjustment on this stopping also
taken And yeah of course Obviously it will
be different It will be out if you
have almost are in and people are not
using it just a way I told ARN
any LSD has emerged from Martin for that
context I told our but otherwise people are
not using You can use our men for
a simple sequence We have one exercise unless
the immortal which is going to behave as
a simple Lauren and it's called is a
vanilla a Lestienne Have you hear about any
loyalist IAM The same spelling has let you
any life scream being and I am really
vanilla You can come across the term called
Many Loyal Stu means you're Elysium is going
to work as a simple ordinary If it
is um your unless them when we talk
about a normal esteem in the sense work
Well STM with memory units like this makes
a difference depend upon what you are What
sequence you what sequence you're going to predict
If it is a very simple like the
first Texas we're going to see the character
to character mapping For example alphabets after the
eight It had my mother when I passed
the A means in my model to predict
be BBC There's not much context is needed
for to understand the site My current pattern
is sufficient right to predict the next character
context is not so important forward for character
to character prediction for such a model Simple
rely less team is sufficient So this surface
girls unless team was needed for the very
little complex like your mobile text application and
all people use the multilayered intestine models This
is water The forget gate Forget get is
working something like this Suppose we have to
context the nation's nice person Full stop after
Charles on the other hand is evil As
soon as the first full stop after a
person is encountered for good Gate realizes that
there may be a chance off context in
the next two sentence so after the next
ended as a result of this the subject
of the sentence is forgetting does its objectives
donations forgetting And in this in this context
Charles will be remembered how much it is
Remember Armand will say like the street like
either remember or not But in the LST
um you can scale that rememberance 80% remembrance
and 40% forget like that You can skillet
one of the important pre processing you should
remember particularly for L s name or in
the very popular data Pre processing is men
maximum LeSage or standardization also But very popular
is Min Max Hope you know Min Max
Today every sample is affected by the minimal
value divided by range maximum is minimal Also
we know that this category has to be
accorded the same rule applicable for like deep
learning model Whatever we did it one heart
in court It has to be done uh
and coming toe because every record has different
world land right Suppose suppose you're solving movie
review data Movie review The first people who
have 40 words and secondly we may have
60 words When you in court you have
to maintain the equal int so for maintaining
the equal and we have to do the
fighting zero padding the shorter has to be
perfect zeros So these are some of the
pre processing We have to keep it in
mind For LST models in the place off
your LSE a model is something it's going
to act as a couldn't live Eight It's
it off dance You are saying you're dense
layer is made up off not as a
simple neuron but memory units You're going to
say as a memory units the two means
the two neurons to memory cells Your initializing
in your real STM layer on your awkwardly
remained same according to the number of category
you specify The now only difference is what
How do we specify the input to the
LSD And you have to be little carefully
hard to analyze according to the data size
so that sort of you will see with
exercise But this understanding will be better What
this time steps water samples orders time strip
or the speeches You'll understand that the examples
No specific reasons you can also use Although
exercise can be delivery functional architectural Not for
every case is there are certain cases we
can say functional could be better but all
the exercise whatever we have them you can
apply functional modeling also more or less it
will give the same reserves Not drastic differences
The previous residents even discuss Vic instead off
for sequin shell Every layer every later you
have to keep it right But not to
start with a simple case character to character
mapping So we held off generated the string
in music I have generated the Zetas It
here The idea is what How to press
whenever I present the ate My model has
to predict Be if bees presented models to
predict the next immediate character So how do
we generate the inputs Come how to generate
the input What does my export is Why
here for this problem What does the extent
What his way Got it Thank you Thanks
You've tradition So your excess water orginal sequence
And why is going to be the time
shifter devotion So we are going to prepare
X and y first That is our first
task So my entire pattern is defined as
a string single stream Now I need to
split What is my ex and orders Why
the delayed wish But you run this for
cell as you know this toe What is
the libraries New to footnote You This is
LST um right Unless CMS other than all
things you're familiar with you okay Okay What
is the first step We do All this
has to be numerically and corded right It
has to be new medically in quarter so
I need in court 0123 and sort after
new medical including your ex And why you
will be looking like this 01 great like
this So character to India and in pleasure
to character That conversion we do the enumeration
here Yes it's okay This is what the
numerical including widow A B C D And
all it is Oh and go to like
this fashion Okay Here What is the sequence
Length one right One word One word is
a sequence length The next exercise I'll be
increasing the sequence Land 233 characters So here
it is a one word What What It
means that you can also build a model
something like this ABC models would predict when
I say be off my model to predict
G when I do I Jiechi my model
to predict Ian this is What harmony What
is the length off You are every sequence
you want to give that is defined by
here Sequence length is one here That means
I'm going to give this mapping first Then
we'll do this mapping next OK No wait
You need output Not the supervised model How
come we thought output it See what this
function is doing This function is just separating
according to your sequence Lent it is separating
the characters Alphabet is what though The street
complete singing store I do I And here
it is I play sequence lint So that
means that your sequence input If it is
a because of the string right it is
taking a means Your sequence out of the
Fed should be I spent this indexing when
your current I zero because you're from Alberta
zero plus one has switched Zero plus one
is one it is going to fetch be
0 to 0 right 0 to 00 That
means it is fighting it So when I
index's zero it is fetching a for the
input and be for the output Similarly it
is speaking for all and finally it is
sprinting water sequence in order sequence out This
is how we prepared the sequence there A
BBC and so far so yeah when I
e zero alphabet off zero is the eight
I forbid Off one is B When I
index 00 colon zero means you start at
zero and stop zero That means you're fetching
you sequins Linda's one When sequence Let this
one What is your sequence out Zero plus
one Great Zero place zero plus one is
one I feel better off oneness Be so
I just taking me sequence So Siegel seen
in fight on When you say zero colon
one upon index is not inclusive right Apparently
it is not inclusive That means it is
fetching only zero The first example Year longer
twitches That's what It this zero colon zero
plus sequence Lint That means what Zero colon
one Right My sequence let this one And
here there is no beginning Index only simply
says that I plastic ones land zero plus
one This time the second character is what
simply exists one It is fetching one This
is you know Colin one This is simply
one Imagine if my sequence Linda's three What
Because it is I place sequence Lint rate
I e Tzeitel sequence length is one so
zero plus 11 But here it is I
call in from the beginning of this number
That is a meaning Imagine if sequence Let
this three What is my sequence Input That
is for the zero index zero colon I
plus sequence industry So zero colon three What
other characters will be fetched You This is
Eagle after two It don't fetch 33 is
not inclusive so far The ABC what is
their sequence out Zero plus three zero plus
three is three which is three delight these
three So these the sequence out But which
one I place sequence lint I eat less
sequence Lind zero plus one If you want
the Glad in D C Says it here
Now see here I put a brace here
Now it's clear for you Uh okay This
is very important The three shaping I hope
you remember for can relation layer how to
present your input Right Expand them Civility Great
So same way LSD him also accept input
in a particular former What format We accept
the important in terms off samples time step
and features then sample time steps and features
What is the samples Total And off your
records Going to six Right total And off
the record that is let off that I
X if you want to check What is
it Orders there in data X No What
is this one And what is this one
This is the steps Ice Okay this is
time steps I take one time step at
the time so we can control the time
strip if I do a time Step three
What It means there are two ways I
can fetch ABC One is a framing One
is a framing another in terms off time
strip Is it possible toe train This one
using a normal multi layered Perceptron model We
can do that way we're doing mapping Only
know one doing mapping Still we can solve
it but there is There won't be any
context context Well interestingly there But any hope
for this data There's no context because your
current sample single only for the in that
case Anyhow your numerically including right is numerically
in court Zero should be mapped onto zero
study zeros map to one one is matter
too shall we Because you have is a
multi class classification problem right It is a
multi class classification problem Still you can apply
a multi model but when it comes to
sequence off input when you want to give
sequence off input how the soldiers in multi
class problem You have to apply framing because
you have sequins off data You apply first
of three samples Give us input to the
modern next to a window ing techniques something
like a framing You take these samples you
to the model next to samples like this
It's not framing here I use the time
step when I specified time Step three it'll
fetch just really courts That is all the
time steps is here it is 11 sample
at the time And finally feature is water
single feature What The member single feature Yeah
1 to 11 Too many were talking about
that right If it is a multiple output
or order in that case your input feature
But here it is a 11 input one
word and put your Queenie for many to
one Mapping your movie record Suppose you're a
pro You're solving This is Ian Let's see
a movie movie review What kind of architected
this many to wonder it That is many
to one so many to one in the
sense how many words you're going to have
in the vocabulary because you're going to Will
the book vocabulary The previous router Scarpa's Do
you remember how many columns were used Those
in that case what should be your feature
length here 1000 Let's start here I'm using
just only one only one word right Only
one column This is what only one feature
that isn't your movie that got movie review
You have one vocabulary Things know each one
is one feature So they're us I should
be 1000 for your If you're maintaining your
rectal in this 1000 means your features should
be 1000 here Number of peaches Okay Next
we do the normalization they don't normalization and
we do one heart including And if you
want to check for a sample because we
have 25 labels are there zeros in court
like this for to recreate the modern So
first I used LSD Um this is something
like how many neurons you select for the
dense layer hidden away The same way you
decided There's no straightforward our relationship Our farm
locker designed this except island after approach only
So 32 memory units redesign And this is
what I should do Give properly to the
listing input ship That's what you have to
tell me Already have included Right Okay You're
Although put labels are included with 25 26
units we helped one different samples so don't
difficult 26 What did the x chip off
One make ship of one one This is
one a ship off to us One This
also 11 and one Input shape is one
and 11 time Step one feature 32 neurons
32 neurons But here I use the term
memory cells same as they or densely number
off neurons And where from where it gets
the input it gives the input from one
single feature with one steps one time step
The shape of zero is 25 but the
shape of one is one except off those
one So what we got after reshaping 25
1 comma one Great I'm not passing 25
1 Come on Once specified here as input
ship next is what you're output layer This
is what output layer Why shape off one
what is the way Shape off one 25
and a multi class problem So soft Max
And you're compiling foot us before what do
you do it in the deep learning model
Samos Categorical cross entropy Because it's a meticulous
problem remaining thing things that seemed We are
running it for 500 e books after running
this for 500 books So 88% ages that
you'd for this model Suppose I'm randomly checking
for one sample because they data off x
four What is data affects for 01234 That
is a B C D e I'm going
to take these input I'm taking years input
How do you pass your test record You
have to reshape and pass to the production
model So number eight or reshape off best
You re shape this and you normalize this
and you pass into the modern or predict
And your prediction is this You see which
note is the highest You have to know
if the North corresponding to yes should be
a year so that it will predict Yes
Okay This is the highest into the poor
minus one All our lives there No Most
of the things are I tend to the
polar minus three minus five minus seven minus
night With this highest this is highest This
is a nor corresponding to 0101 toe There
is many a B C D e f
You haven't got the high school so I
have supplied He's the input So my model
throws out Put us Yes but we want
to see actual numbers right So for that
we apply our max when he uses our
max to convert back from your categorical to
your original class So apply our max You
get the index on again We don't want
to see as a number as original character
I want to see his original character So
in digital character conversion off the So any
check results in the cell you can see
if when you check for So when you
supplied e f was predicted I ended this
Lupus creator to check for all the patterns
Okay this Lupus created to check for all
the patterns for pattern and date I exit
one by one pattern It'll check whatever we
did it for One case it is doing
inside the look right it is doing and
say the Lupin dispiriting for Yates predict B
for be It's the wrong prediction right For
being so predicting because it's not 100% accuracy
it is only 88% there There could be
some mis prediction or so for C it's
predicting Be correctly for the year So 88%
it predicts correctly What is the difference in
the same problem with the different operate Slightly
different approach What we one of the changes
in this court that alone you see Look
at the Alesci um to learn three character
time Steptoe One character mapping What is the
changes All of him where you see the
first thing Sequin lenders three Right here The
sequence Oil industry for the sequence land First
we generate the patterns Let us see what
is the x and y for this So
ABC it would baby like this We generate
the pattern Your input output pattern like this
is the X And here this is why
Look at the shape No Schapelle fix after
reshaped because we did the reshaping Here here
we did the sheep Number of input You
have only one feature late This is audio
only one feature What then But But this
is samples This is time Step So this
is sorry This is samples lent off their
Texas The samples on sequence length resort their
time Step three times Sort of three and
features one But ABC together It is one
feature ABC together is one treated Something like
a single word Suppose you passes some good
Like for example Uh what time Suppose you
want to type What time shall be meat
when you're typing What what is 11 word
One feature So what time together A new
type means that this a million for 21
output That is for the movie reviews and
the movie reviews multiple words You collection off
words you give us input and output will
be single output Right In that case how
many words comprising your features That is what
your features We don't design Classify your review
with the one world right You classify the
review with the multiple words How many words
you used toe to represent that the review
is what your features Is it here Moving
in you yet Of course Knots Literally not
after you build a cabinet once you build
a vocabulary And what in a previous routers
later If you see the first two news
group has only 86 length and the second
one has only 54 Land 3rd 120 length
That means we have to What we We
have to pad the sequence Fixer number off
length What is the fix Ellen to be
Have you stones And right So that is
a lengthy Harper use because every review has
a different words right Your features will keep
changing right for that you have to part
with zeros and it made in uniform Lind
That's what we use a function called part
sequence using their part sequence If the shorter
one will be pandered to perfect with zeros
Even here for the next exercise we do
the padding here next next exercise What What
changes I have done here Here it is
I have given if excellent right all three
We can also playing with the variable in
for example when I percent a my model
has to say be BC means my model
has to say D BCD means my model
has to see You can decide what is
the maximum length I can fix maximum Linda
I'm going to go up to fight This
is a very turbulent really Bulent sequence mint
So this is a lento for single So
when I say maximum length this fight means
my feature length is here it is but
still it is a single feature but the
length off the each feature is fight maximum
Lynn despite it's our user choice No any
any size we can keep it So I
want to generate with the maximum of five
mins upto for violent You can check what
is the next prediction What is the length
of the sequence in case off How do
we define output Let's say for a review
data We have one class right now That
is for ah sentiment But for something like
word prediction next word prediction Okay We have
to provide the predicted worst make set of
words And that's what this is what the
next word predicted No it's tough border This
character Yeah So for training the model for
input we have to give the predicted a
set of words of course the combination of
words the way we have to get that
Texas Also we have We'll see what the
word This is a character to character work
overall solvency So I understood the shape now
here because the time step is three What
is the input shape Real STM Here This
is the only one change Almost a including
what is the shape of an X shape
off one So here the dimension is three
common one three times Step one feature three
Come on one Let's start the quarters state
There's no change in the court So when
we do this for 500 edition This is
how model is predicting the sequence Again we
have some miss classes Miss prediction also Yep
G hatch this is gonna be f geo
tonight Almost handled up Okay Because you can
see the number off boxes given high for
this fight Those and a focus given So
next one variable length input sequence What we
do is what they're we have given in
Fix it right Zero colon I play sequence
that I have We have use go random
and teacher So every time it will it
will check Take a random in digital value
for my random indigent May take three or
four right So the length off the sequence
you instead of passing ifixit value you're passing
the random indigent So sometimes you take three
Sometimes take one according to the But you
fix the maximum length this this much within
that number on leave and I'm into just
regenerate the numbers So in that case you
can generate the using a random indigenous we
can generate the sequence mapping for the variable
in because we prepared the sequence from the
data See here we have used the starting
and ending as a random Brandon That's the
difference Here we are using this random statement
starting index And so this ending So here
it will take according to the START index
and index it'll take random combinations And this
is the These are the different combinations it
predicts So your input data for maximum sizes
used this fight know where it is More
than five was taken because maximum length is
defined This fight So what are the combinations
to the maximum for you Give us input
What other changes in the court here Have
you noticed this is new right Why we
do this What We do this Because every
input Norden the fix settlement because of variable
And when it is a variable length we
have to use this convert the list off
history and part sequences So data X max
length is Max in this fight So there
are certain words are only single character will
come or two characters in country characters will
come The remaining location will fill with zeros
that sort part sequences Now you're and there
will be everything will be a unique size
So a violent fix it violent So that
is why we need to do this part
sequences for unequal length This You have to
obviously do it for movie reviews Connect But
you want me individual record individual record Because
your individual record is represented by Victor eyes
Right It is effective Iced What do you
want to do next Classifications Okay it's That's
what I'm saying No you You have exact
number off length for every every record right
Every record is talking nice when you do
that organization Every ah review is the presented
by so and so over Fixate vocabulary a
number of column in the mattresses Because there
yeah there we are using token is that
organizer is doing the job So here we
have not to use the localizer right here
We have brilliant man We are doing with
art sequence option They're your fixer Length is
maintained by your organizer itself because we specify
the maximum sizes 1000 for the rotors data
So your maximum columnist does it Not all
the 1000 will be filled for every record
Yeah You know Dilbert He had also individual
world right No no individual word in the
sense What is my dad Texas What One
of the combinations It's produces That is more
individual words Because we are generating the combinations
right Because we are synthesizing the input This
is not a real time data so we're
synthesizing their data So when you're developing the
data this is one record This is another
record This is another record This is another
record Each one you're doing padding when you
do the padding Maximum length is fire Right
for this This is 00 God Got it
Okay if it is a sentence suppose you
were recorded symptoms You will be doing rectal
ization process when you exercise you Your data
is represented by fix it Number off Woods
This is not like white This is not
a quick Yeah this is a source data
Okay this is my source text Okay How
Collection off words using those source text were
using the function generate sequence to generate combinations
So using the generate combinations region date the
sequences So for Jack and Jack on and
will be one combinations after an Jill world
a world map it just it is not
doing when will be mapped up That's paralyzed
mapping you will be taken as generate sequence
Well you just run dysfunction and see the
put off day takes Oh see What What
are the process We do it here See
her first year of data and you're passing
your data to the organizer Well you're pausing
to the organizer Well so you need to
get the vocabulary from this total lack a
very unique words Repeater Don't be countered if
you count the total number of words 1234567
89 10 11 12 13 14 15 16
17 18 1920 2124 25 But the length
of the capital shows only 22 because the
repeated words it has kept my vocabulary length
is 22 So we are applying this organization
toe their data And this is what you're
in quarter data like transform like scaler for
scale a function we do this scaler don't
transform like talk token I said this is
where this is how you fix You're fitting
He offered in this organization on the data
What is this What is this result represents
each off the vocabulary each other e two
orders Your organization has given some random numbers
to eat words but you don't repeat it
That is the way that organization is given
the random numbers for each word is in
quarter numerically and everything Is your collection off
Probably Right It is in the list That
is what The sequence you're in there Thing
is in the form of a sequence That
is what The texture to sequence study Yeah
Jack is included Us to jackets and quarters
too Me So what we do here in
this The word The word sequences so sequences
an empty list and quartered off Check your
sequences It Any checked sequences Don't you want
to Is Jack right Does And corded These
are the whole combination ist later Like a
to B B to C ABC to be
like the mapping with it for the sentences
before you mapping first be and quartered to
the impeaches And then we did the mapping
So the corresponding word if you related after
10 11 Lamentable There are certain places starting
after 13 to welcome So all combinations generate
So all sequence region Rick So that's what
we knew the richer corpus So that is
why this is what small three lines of
four lines but for the applications like whatever
we use it in the smartphones they have
a very rich vocabulary like they have taken
the Wikipedia vocabulary completely chilled vocabulary What are
the combination of words will come when you
put no lax and lacks millions Off combinations
will come So all combinations for every word
you said it will give all three combinations
three or four combinations It shows so like
Miss your model is trained for all such
combinations if only for this train That is
how your model is going to pick up
that among that half hollow What other frequents
combination We use it That is what we
were predicted By that is the real production
right That is what mortally that we have
People are developed that model day today production
is what it is A relationship You're using
it in the mobile phone But data protection
when you key in this world as input
what is a modern throws outward So very
frequently these words alone it is flowing Those
words will be repeatedly showing for you So
what would you ask Oh yeah According will
Come last So So still you have not
presented to the model so we have included
the data So what we do next we
the presented inform affects and wait because the
sequence is a dessert list Right to lie
motionless All the first train please In zero
column is what your ex All the increase
in the first column you're mapping to white
This is what your ex this is your
wife Your defined that map Then you're all
Portis too categorical because Hominy can't agree We
want to classify because it is a Each
output is I can't agree Date is a
unique category So if you're converting to categorical
and you're defined your model you're passing vocabulary
size What is the capital of size 22
So when you go to model here's the
mortar You help us to a capitalist size
22 So So actually what we do for
more business just by mapping a single number
let's say 14 map to it is a
good idea The 14 is represented by Sten
Digit Victor like one cross 10 director to
also represented By then did you really valued
victors That would be more realistic It's tough
specifying one work There is a one sequence
in terms off single number you are representing
with the 10 numbers No which will be
more robust No instead of designing one number
and say the two closer while is No
14 and 15 on Day four if you're
comparing 14 5 10 different values means this
will be more robust rate instead of your
comparing 15 and 14 How close is this
that single one unit but 15 years represented
vaginal fruit for release in a prisoner that's
150.45 point 36 point Fight like this on
14 is represented and you have the differences
more every little bit unique That means each
other values and quarter the former For Victor's
multi directional vectors it's always user choice you
can represent by 10 2030 That's what So
here we passed in Generally for a this
is a small one single word right But
for movie reviews and all people use are
you to say something like in boardings frozen
1,000,500 You can specify long victor so granularity
will be better right It will be more
granularity better Granules in the sense water in
the example a quidsi which is more granular
before including it will be more grand A
letter after including Iran will be more grand
Gradually the sense what you're breaking into a
small little units in the former for director
you cannot you granularity That is what is
embedding will be doing it so appreciation will
be increased That's a simple idea Why We
noted because off the position it will increase
the position off your data so granularity will
be increased on this This is the only
additional thing They do it here And as
usual your number off units here A lesbian
Coffee units talk Max Everything reminds him Okay
I initially replied the model as a PNG
file architecture it will show the architecture and
then three fit the model the 100 books
and after 100 books the model is evaluated
sweeter It has frittered on X and y
with 100 books on your checking Evaluating the
model for the world Jack Okay You're evaluating
the model for generate sequence for model because
Jack has to be passed on to the
token is right Because you're passing the string
it has to pass it on to the
organizer All the new medical condition has to
be done Embedding has to be done internally
and it will compare with which is the
closest And it has to show the what
does the six number It shows that after
Jack order the six Next next words should
come after Jack What are the six next
words After Jack When you say three after
Jack what are the three next words That
is what in mobile when you type one
word it'll given for three options and showing
right three or four options after this work
What are the options you want to display
Six Next to sequence I want to see
That's what you see So this is I'm
taking for Jack input Well yeah Six possible
based on you are independent mapping is you
did right So one of the closest to
prediction for Jack If you want to evaluate
for single that is it will do it
for multiple words right You want to predict
a multiple in sequence if you want Oh
check for one single elements You can specify
your in text What is your in text
I'm giving Tumbling Tumbling is my input work
under your model is going to predict for
tumbling What is after the predictor word is
after It's predicting after for the input tumbling
because you are playing here What's your data
is falls Don't know the organizer and then
you're doing it You're doing the job manually
Previously it was done automatically This is manually
or checking for one single 100 iterations Right
Actually see this one I have to state
for 500 reports but my 100 reports will
take a long long time So I tested
this for 500 folks I think for 500
books When you run it will predict after
correctly But the finder deports At least it
takes for me Daughter 15 minutes it took
for finally folks Yeah What are the six
editions This is what one Only one single
value I'm doing it I'm just lending for
one single actually See There are two Testing
is done in the story One way is
to check only one immediate work Another is
what to predict The sequence Okay 121 on
one Too many There are two things If
you want to check one too many you
have to call this function then date sequence
function So what happens your modern not predict
classes is rooted in the far look It
is given in the far look So next
six sequence order the number or sequins you
pass here Your model will check because you're
calling You're calling Generate sequence function You're passing
the model What model Order The model You
are designed That model you're passing by calling
this generates sequence The same model is destroyed
in a two different fashion So 1 to
1 checking means I have done it in
the last Isn't that is what 1 to
1 chicken One immediate word at a time
when you're calling this function means your mortal
dot predictors called inside this six tanks What
According to the number off things you you
put it So it is giving the output
six possible next six next to six year
next to six in the floor sequence Not
necessarily Always used index I'm not using I
just You generated the four Look just for
a contract I'm not using the index just
for accounting purposes I use it empty It
is an empty that this whole beam apparatus
How do we play in the morning We
have trained a marvelous one word to one
word Mapping one word One word Maybe Yeah
that sort is 1 to 1 Mapping with
did it That's for your movie reviews Many
to one Mapping many words you will be
having many words will be mapped onto one
outward one output leg Good or bad sentiment
Good or bad labels you'll be giving it
So what Unless your movie review also will
be working like this What is only difference
here One word Input one word or foot
I'm multi class output right Actually here it
is a multi class or for movie review
your importers multiple words Input on output is
going to be a single two classes A
the zero or one So that is what
the assignment asked for You have to do
it You have two choices you can do
to the LST model Also you can try
with deal model Also uh this is for
the classification but this is ah sequence Prediction
This a sequence prediction Why it is a
sequence prediction because you're output almost You can
take it as like a multiple multi class
classification Also you can say but it don't
make sense right It don't make sense Multi
class classification But here you help you help
retreat This is a sequence prediction Which one
Yes In same instead Awfully See your told
what does Ah here it is a word
there you'll be using your or porters are
labeled write zeros and ones So all the
labels you have to pass for White either
01001 like that So here are $4 over
again The word is numerical and corded right
14 15 like that instead of 14 15
16 You'll be having either zero or 101
Uh de plenty There are C It's impossible
to touch all the models Correct No even
an LSD A model himself What We did
this A sequence prediction One case we did
And Ah What we did is Ah ah
Classification classification is almost like a multiple multi
class classification You are going to do by
another classifications You can also do see Imagine
if I don't do this in digital character
conversion simply I'm plating my input like this
Imagine my data is like this Can I
use a listening for any other purpose Numerical
data protection right So for stock market for
our passengers So you'll be This is what
My ex What is your wife The later
response right 15 17 18 20 to 46
87 So you feel trained The models That's
like your time series data We can more
than like this Uh huh That how maney
time steps you want to take that you
can define right If you want to take
three times statements the base don't immediate past
three values See How do we did For
example the ABC The order we predicted be
based on the past three when I give
the time Step three Similarly you were your
stock market data You have so many features
Study You have like this work Giggling Great
day One day two day three day four
day for day six end up You're going
to predict the four today based on the
first of three days This is one mapping
right This is one mapping Uh yeah That
step says the banks step you hard toe
Check it So that also is Ah it
is a dentist One exercise You can just
go through it Self study I I think
for now it does not shared with you
I last program office to share it but
timing quickly and show that for the new
medical later here you have to specify how
many data you're looking back Let's say more
than you're looking back Three off four samples
or three samples that you can tell in
terms of time strips So input shape here
for defined We're doing the compilation because it
is a regression problem So instead awful categorical
cross inappropriate and passing means square Well it's
on the list other things that seem And
since we are going to use this as
ah prediction that is a numerical value production
regulation I need to do the inverse because
I have standardize the data So do the
inverse transform to predict the rallies Okay this
is the kind of foreboding get It's a
cell Surely you can easily go through Not
much difference Only thing here our data comes
directly is a numeric The X you define
and you have defined as a time step
delayed tuition so excellent excellent you define And
this is the data upto this we have
predicted for the next door next month We
help predicted and Also the root makes question
is given so it is not 23 The
embassy score is number off in terms off
thousands of passengers It is predicting the passengers
in the thousands out of 1000 sof passengers
22 passengers Is there off 22 passengers Model
is predicting thereafter under two passages Which one
Yeah that is that is that is the
thing They do it for the chaos classifier
and Karasin aggressive But in the LST um
they kept me this question That is water
the plus or minus your I looks the
all your orginal samples and predictive samples superimposed
orginal samples and predictor sample because this difference
is producing right this difference is producing your
autumn asi score and why the skull according
is so separated here This is actual and
this is predicted On top of that you
hope I can solve this using a normal
regression problem If you solve this as a
normal regulation problem you do only have two
prediction but additionally what we did we predicted
for the future sample source So future samples
also predicted and based on the past history
this is separately color quarter to show that
This is a future production Future steps like
future Step after Jack you wonder six next
to six right Similarly you can specify next
Hominy samples You want to predict that this
world this is highly dealing with this color
Which one This is time cities only know
I'm almost picked only the difference after only
two passengers study Normally it's not fabricated data
Cereal eater Real data It's a year later
It is used for the benchmarking So because
off the power off less name only deplaning
deplaning capability If you If you do the
same thing with the arma model traditional arma
model you may not get the score motive
You'll get motive Traditional time cities after aggressive
model and all You'll have mortar Definitely So
if you can think of using this for
stock market really interesting Like stock market you
can use this model for the prediction I
think so I put into harmony lags You
want to make you suggest you have to
look back How maney data that I only
have a specific You just try the same
data You can work with them Different data
sets If you have financial a stock market
data off the market values and you try
to feed that with the delayed under you'll
get some interesting business No for generally an
immortal mission learning or depending morally should have
some decent number off historical data So that
is a gender route Then we have to
see the characteristic off each morning then accordingly
have to find you Okay This exercise Ah
just leave it for yourself self Really I
just go through it So now almost all
cases we have touched upon numerical value Also
we predicted only a mission Translation many to
many is out off the context off this
course that see NLP itself is oh separate
field in Buddhist tutorial We have learned about
natural language processing What is national language Proceed
on How can you do some of the
bigger manipulation using anything on what is back
off What's England on defies on Also we
have looping situation that because I only knew
really took a long shot a minute And
if they want to learn more on this
new topics all this advanced off its then
please goto Great landing accuracy platform Very well
did almost 80 last three courses on after
completing a poor second claim Your secretary That
is also three off post Thank you so
much Thank you So much Toe seen at
