the short definition of computer version is when
a computer or a machine has sight, to
get a little more technical computer vision is the
process off recording and playing back light fragments
The importance off computer version is in the
problems it can solve It is one of
the main technologies that enables the digital world
to interact with the physical world Keeping in
mind the importance of computer vision we have
coming for this country to region tutorial Now
before we start for the session I'd like
to inform you folks that we have launched
a completely free platform called as Great Learning
Academy where we have access to free courses
such as AI Cloud and they're still marketing
So let's get started that we talked about
last week Waas How exactly sampling takes place
here Yeah So if there is an image
I hope everybody is very clear If I
say a made a 16 megapixel or if
I say a major streaming up Excel or
if I say marriage is 64 bits image
for example or 6 44 birds 64 births
order What does it means here There are
more picks It's It means thumb Eddie that
is holding this is pretty big It is
a place 16 meter and here the size
is very less so There is a problem
in your current project What is the size
of the image The size of the images
30 Douglas study Do what do you have
deepened when Gracie Big 30 logo study do
is the size so that you do Indeed
What we do is what when ones you
don't do for Yeah So it is OK
for me to build a neural network which
can take 10 to 4 neurons as any
And it's kind of you know do one
print down on these things and finally give
me a glass of fat is no it's
OK Not a problem But But what if
now this waas 16 mega So when am
I live like this Two It became 16
million Now is it possible for us to
handle this Yes or no Can I have
a 16 media input Your own group Nobody
should It'll be a memory problem It could
be a competition problem Cost factor You know
a lot of things comes in tow Picture
agree So in this case What do we
do is we don't go ahead with simple
MLB on F CNN rays What is MMP
Correct My Denia Marty 30 might be layered
perceptron Okay which is nothing but another name
off F CNN itself So please don't get
confused I've seen many How many people they
say MLB an FN internal They mean the
same thing But stepped on is nothing but
a neuron Whenever the neuron goes into hidden
lay that becomes the perception or knows why
But this is what this now this becomes
very difficult to compute So now what are
we doing Is what if I can We
just saw the Arab bite Encore Beautiful Bite
and Gordy we have I think it is
working good What we did we weren't Don't
do a new software or especially me When
don't do a new software and regard up
diminished version offered which cells which does the
exact job as ordered Does I will say
north as effective is this body is At
least I can see if the new families
you were able to let you 70% of
what it does I'm really head for later
That court extradited Gato Why should I waste
my time on court Agreed Everybody is the
same thing you can imagine about images What
if I don't want to be scanned The
whole image So let's say this is an
image and that is one person's turning in
the image Here Ignore my drink Yeah yeah
there does that This is a person and
that is a flower Over here There's a
flower quarter we have and say that is
one picture the picture again There is an
image here Something like that Let us say
me have So if I ask you what
is What is the information that you guys
can deliver You definitely don't mean that There
are three sports of information Plus this that
is an object which is a frame There
is an object which is a flower That
is an object lesson which is a port
that is an object which is up face
that Did you wind it out You know
See you What for So if you bag
this up if you remember What do you
mean What do I mean by tagging Your
current project has got tag ings agreed he
had also We have bagging Ximen say object
I haven't seen person I would say object
Okay now the distances if you're designing Very
nice If you detect a person ordered this
Yeah If I want to do this I
don't have to scan these at the empty
parks Open people up getting I don't need
to scan this What if I designed small
filters Okay what about this What does this
really does Have some but I never just
like your random awaits that you have chosen
or your scene I usually let us say
these are some rates some frivolous which when
you multiply with the whole image we are
able to get a diminished version off this
20 So there is a possibility that what
if I multiple light multiplied this figure out
of this OK and I will get one
I will get Lord off diminished feature maps
like this I can call this as a
feature you can call This is a feature
feature Freedom Now what if I used this
as one image Now this could be around
say four cross for image for example What
if I could use this into mindfully connected
mural Good work classifications What if I could
use this separately This separately This separate it
is separately So I need to run my
fully connected neural network 12345 Pets toe identified
it fire objects a big so that and
running neural networks megapixel times I tried to
find out some features so that I will
be able to classes Yes Krishna Pierre Offic
So this is what is your nu uh
needle convolution Neural And this is what we're
going toe do down the line So any
convolution you really looked So whenever I say
filter people should completely remember understand that we
had often award convolution was this There are
a lot of things they're screwed on the
line And always a convolution in your leg
is backed up by a fully connected nearly
So this is not leaving us anyway So
whatever you guys are than the first session
our first model will remain with your voice
through this complete if convolution Morning So now
moving on what other things I was talking
about So I redistribute directly that particular page
okay And let let me me fail now
It would not be rushed with a good
amount of friendly So these are my favorite
us which I am giving it to my
image Yeah So my white portion of name
because minus one and colored portion where one
is returned have a good one and I
multiply I'm going to get a diminished world
journal This if you want to see what
I mean by the minister version this 93
grocery which is nine numbers I have diminished
to one number 12.33 if you still want
Norm Listen Howard doesn't actually one small gift
which doesn't Okay just observe the gift One
that I tend Site Ah five cross five
matrix is now diminished Toe three across three
metrics with one convolution Anybody will give it
this How we got this one number I
would Sure it's a formula Very simple formula
manual Multiple multiplication is needed Okay so yeah
So this is what we have Not Good
Now when we did this we got teammates
is like this Okay post that this matrix
whatever we got we would transfer it to
an activation function Said now I said as
you pass real you people know that zeros
and anything below zero will be omitted remaining
whatever is therefore be process story So now
what This isn't or pretend whatever Everything that
means it will be moved out off this
matrix Negative numbers we want There is a
technical fully max bullying will average pulling the
radio I'm still not happy with this I
want to study diminish this image So this
is now I was a seven cross seven
image Now I would say the size of
my friend that was too close to So
in this case I would do a Max
pulling on this I would say find out
the maximum features out of this four Maximum
number out of this for basically one This
is quite Max polling So well do cross
to matrix now is represented by one cross
one That means I'm rewriting the whole matrix
by four Indeed guys Simple He set off
Max fully You can even though average fully
our sporting meets taken hours of all four
What do you know The All right So
do it past this filter to each and
every portion off this matrix and good So
this is what this So is it always
toe by toe that we have to take
It can be more than that to be
able to find your final Max pulling thing
But usually we take to buy do because
it is easy to decide No And let
us save Predicted by three It wouldn't be
this divided by nine So there's a possibility
that we might lose lot off pixels are
We do agree We have lost some pixels
here Information I guess Now why is this
So why am I happy with this one
Questioned All off you It's a few people
got this pulling apart off it We question
What is the use off doing Lee We
are already happy We want really like Diminish
the Matrix They're happy with it Our new
leg broken Take it still Why are we
doing this organization Sunny Oh my lord Enough
Uh yeah I kind of got it But
why I still buy I want more you
to prevent over fitting Kind off Mm No
Not exactly See I OK I'll give you
one really simple example Say I have a
Currently I have five batters and Billy Okay
All of my Ayman back is only now
from this five match if I want to
Let us say there are four candidates were
the top performers For example There is a
candidate number 800 number B can't remember seek
and somebody from all five off that notices
something Now if I owned a re present
how a batch looks like on an average
or what is my strength off my match
Can I say I will pull out the
best out of these four Exporting or I'm
taking a result of this foreign Correct Yeah
So what does it mean ISS This particular
four things when they come together they kind
of re present a single region So it
could be Let us say and I broke
off a person and I brought a person
like this Okay Where Where If my scanner
is going onto it If my seeing this
is my choose one light color See This
is my convolution here For example we're talking
about I think this is the face Okay
Imagine this is a face Onda Uh say
these are my eye bro's onto that Okay
Now what am I doing This That issue
my scanner is currently hovering over one off
the Eid rose Something like this Yeah No
What if I'm doing Max pulling onto this
What is next for You knew We just
saw It will take the best feature out
Do I need all the features here or
do I need the best feature out of
How can I say here the best feature
is nothing but the black portion here Never
steak and pull it out saying that when
I scanned through this I find this particular
location the color off these images Black No
don't know So in future when you're giving
a new image when this is going through
a newly made at this particular location If
Maxwell imports out black that means is to
imagine this Sport QC Let us say this
person and I look scary Let's say this
person has got one or two calls more
on his face and many in future You
combat it at this particular location Definitely This
mall is going to be there So it's
going toe Understand that Yes it is a
seeing image a very high level debate I'm
still like we're not by people Just trying
to understand your ways that sometimes what happens
is when you try to think give you
removing the pixels Not actually were being the
best features out off this piece is Thank
you Doctor All right any questions under this
But the conditional we might lose the information
right And images mostly It's like black color
is more dominant in anyways Right So if
there is anything in that is having less
information but also significant information we might lose
it right Yes kind off Kind of Yes
So that's the reason what we do is
we strike this the we make sure but
our filter that we have design here it
goes through Come on get me Change the
slide Yeah I will make sure that the
filter that I have resigned but so far
I make sure that I have been a
good sample e I'm not gonna waste sampling
customer And also I will make sure that
my filter goes through almost everything I don't
lose any of this my friend Get my
big night with everything off Okay So you
have kind of flight Yes When we try
to diminish something it happens But also another
side When we over sample another and we
do a good sampling We will not be
losing any pixel in this case All right
See What What do I mean by Sampley
I will give you a very good example
Let me open up an image if we
have image Uh yeah So there are chances
that when I do some type of sampling
one pixel could look like this And also
there are chances there When I do a
very good sampling one pixel might look like
this Also if I tried toe more sampling
one big son could look like as small
as this dot So if I have good
samples like this more samples it will be
easy for me to remove So that part
of it it's OK Because if you observe
here uh the same color propagates almost through
a complete top part off the sea A
greeting Yeah So if I remove fighter been
off them doesn't pictures Yes Yeah I gotta
be very frank on this Other people able
to visualize a because if we say like
if we don't question right now then down
the line in second and third We were
not going to go back on the's very
clear on that whatever I'm saying that he
was able to understand I mean I just
have one question personal How about the samples
Eyes they like Because we might have like
one lack of samples provided to us And
that did not However it might be an
individual image which might not give the territory
presentation off that that would be like No
this coming on the left or it should
be under light in order for um augmentation
and every flipping and all those things So
how do we ensure that the samples are
writing place as acquired for our analysis Because
we cannot check all the one like images
if we are provided with that right Correct
Correct So for that we will really see
something called Matrix Blood in in one of
the weeks I think it's almost last week
off This morning we'll try to find out
the distance between the witness How far they're
similar images but how far they're from Okay
very good question But let's let alone Yes
we'll cover But I'm clear on this creation
off because this is one topic here We
generally of my matches No they don't get
it That how By filtering woman people in
your filter were able to get an image
Okay good second application I'm sure Why CNN
So if somebody will ask you why exactly
CNN So in the beginning you a very
simple example is many But I assure you
let us see if you look at a
car you look at a car and then
a c The car has the front part
of the cars like this They are one
minute I'm really sorry Yes Say the front
part of the car has got two openings
like this Yeah on definitely We know what
carted us notice And this is a BMW
for example Just imagine this BMW Yeah So
do you And check the car Let me
check the viper Let misunderstanding let me take
the whole Let me take the seats No
visually if you want to identify something immediately
from the border down the morning when you
look at this for us you're going to
know Okay this is nothing But uh uh
What you say BMW Also if you look
at the already part of it right on
the front you might find four months So
definitely this is enough for me to identify
image Same concept You bring it on computers
You don't need to scan the whole image
If in case you wanted Gen Brick scanning
happen let us see it I give you
a very good example I am currently working
on this project We're This is for my
gated community that really I currently lives So
we aboard a nap Corn My gig on
what does is my get do is it
helps us toe tractor vehicle So somebody's coming
in our community It has to report And
there is a notification center The owner that
somebody is something you do to allow our
we say Yes So everything is manual Watch
the security guard Esto no Get the name
off the vehicle Type of the vehicle Name
off the postal order Fix Now what we
have done is sometimes also gets unnoticed because
the personal stuff from outside I don't know
it actively walks into it And security as
you know today the part of the company
Sometimes they look the one notice So what
currently am working My owners Let's install cameras
Okay Which will try to snap a picture
or roast is incoming cars and as soon
as they take a snap a picture they
should be able to locate where the number
paydays And if you remember your current project
that you're doing we can easily identify the
numbers out off it And from my central
database we get to know where that this
is This car belongs to the community or
is not in the car you heard is
not in the community the gate literacy away
here should not That could be an automated
gate which will open or close because off
this trick us for example Okay so so
far I am able to identify So if
I think of it cam and run around
the cars yet I'm able to get the
number plates I'm sure why Shorty when she
lays understand what I'm talking about So probably
second and third we got decision I'm sure
this were we'll take a webcam and been
trying to identify objects from Okay good So
this is what it is So what I
have what I wanted to show you guys
here is sometimes in company virgin You don't
have to identify the whole of it You
just have the foot was uncertain Part of
the image that's all If you get it
do it And how do we do it
CNN will help prostitutes in CNN We don't
understand the whole thing We don't need holding
as an infant We scan certain parts over
it and we take it is an important
sometimes some of the parts are perfect image
that we want Good So this is one
example off CNN I will give you one
more example of aliases used Now it is
Ah one of these men atrocity that our
malls in more than a parking lords And
now right in front off the gate itself
The display How many slots I am beyond
which floor which level and what size Good
idea what days to do it is to
put sends us over here So if there
is a raid on the thing it is
it is God that is a car if
not business Actually the senses are very costly
on during maintenance and all the good destroyed
they have embedded to Jimmy of sophistication Mass
like has to be for over a year
So now what they do is very simple
Neighbor camera if they find some other color
over here that means there is a car
presented the week The slaughters on empty accountable
museum What is this This is constant men
Tick segmentation This is your victory contented We're
goingto we don't care what it us We
just see that that is one background color
and there is some different color on top
of that that over this is occupied I
don't need to know what card as what
color it is what shapes sizes It's not
about application Good So this is the convolution
Your networks will come over here Good Now
coming onto the weekly country So this was
an extra Oh So all we do is
we take the image we go through convolution
because Raila with water pulling again we would
convolution Ray Lupoli If you still want more
you can go through normalization Normalization normalization Novelists
Yosh If after this what did we get
There are two layers of evolution ated What
did we get A bigger was conducted this
morning Next work next flattened them and wish
them wonder Fully connected Neural network That's all
is your CNN This is your flattened it
I really know this 0.66 means point manufacturer
It's an extent screen short So he did
not I did not want a similar just
over here Okay so one 0.55 point forward
their flat and completely And that flight anything
goes here And you know what happens here
and how we classify That's that's always your
kind of You shouldn't even be talking when
you bundle all of this together This is
our close Like he made convolution pulling convolution
flattening and your fully gonna tell you so
Krishna lower question How many convolutions and pulling
short radio carriage Is there any recommendation or
how we don't know Mine wins when to
start doing Oh flattening Yes So there's a
formula we're going to see down the line
which will help us toe through this But
frankly speaking every value Um what you have
to do is you have to determine what
should be your Inwood here first of all
and from there we will board on Billy
Okay So if you want say 64 that
means your flag and should be 64 And
if let us say you are having um
the end features over here That s it
10 windows over here Now Each window should
be What if you divide 64 by 10
years gets explained for which is not a
good life So in that case what you
do is you make it eight What is
that So 88 64 So it could be
too a cross for each one You can
say to cross too many number of times
I think to close to four for today
Uh anything any number of times But I'm
sure down the line how to do that
Nobody Because look and you repeated I don't
think I got that the What do I
What was the question here What's how many
times I should do this less What should
be the signs off Actually panel all this
stuff so it completely depends on making put
festival So what you are planning here for
there to say you are planning and you
put on 64 if you're flattened Data 64
What What What How many friends as I
should have This is what That we're should
this Yeah So how do we do I
use this now so take the reverse engineering
Cancer never saved my Each fritters go across
to image So to grasp how many numbers
I have for how many more I need
16 for that 64 right No Yes or
no Yeah Is extend for two extra thing
that Yes I need 16 of these across
to Okay Yeah So now good of us
If I need 16 of this home it
should be mentally How much should remember the
This will give explained I go down the
line but this is one major Let me
do it Also one hand for all of
us Very less Models were going to use
this We're going to use ready made models
We g lx Nay Uh who will leg
mobile and rest There are in in our
CNN We have ready with more Let's huge
big models which can handle up to six
million parameters Very few misses the RBL Okay
because now no we don't want big border
Not something like care He and I Any
claim toe You already have ready made models
by some of our famous companies Why not
do important news Okay good off it So
or are the whole model What you will
know you will You will be given an
image You will We should be able to
identify First off on the image You should
be able to detect the image last fall
and was detection You should be able to
identify it Waters It can be a car
dog Human me cat object Anything we take
you we should be able to do it
So just already say Oh beautiful A few
this session are this morning We will be
around 7 to 8 weeks Very frank It
has got to projects into back to back
The first project will come after your fourth
week Failure to detect an object you to
create a boundary around doctor in the second
project is your protect Whatever you have created
bound the around quarters What type of object
Good So by the end off eight weeks
seven c on those two months you should
be able to be good at computer which
used to be able to at least implement
basic level of competition on your from your
site from scratch plus something extra from my
side I'm sure you a simple versions off
this I don't want us to go in
court Huge It's good Nothing wrong You do
it But also what we doing Industry side
of true small accords Use ready made models
to do Plus I've been sure Something extra
It is not covered here Just called with
you What is the major in continuous See
you Yes So that could be a very
good application Especially if you are from manufacturing
industry building industry or I will say majorly
under construction industry some some last time I
give you some examples from my side on
protesting in desperate So still we are exploring
how we use this This opportunity Okay good
Especially if you're from retail on manufacturing this
and help your And frankly I currently interviewed
for one off startups Who were you Um
I'm seeing manufacturing on They went from acuity
And so they were a very big product
from one of these large automobile manufacturers where
they want Oh pinpoint from there What is
a manufacturing line Which product is defective just
by looking at the image from the camp
so that this is that could be a
lot of high definition cameras attached earlier What
these days is to do it is to
pass this stuff from you ease or they
will pass a laser like to check the
deformities But now looking at the image itself
they want to product Okay Any sophisticated bullshit
this good So day by day this stuff
is going on It's warming So by let
us say I really one more thing That
timeline for computer vision is at least take
for your way Six months to get normal
brothers and start a play Really So by
then you can say this will be a
learning call So please be very patient with
her It is not a single concept Okay
What is it We discussed why we discussed
Because off you made size costing Or regarding
this more or less This is what we
saw in my hand It an image may
buy lumber Earlier inmates you take off each
other out off this on diluted flat and
it up put it on the way fully
connected to your network and get the protection
If you observe yearly a prediction called Khar
Yeah Good All right Oh how I just
explain you how we do it So please
remember one thing This is your static layer
forward propagation live This is your forward and
backward perforation there for money All right so
there's a classification that this is your future
Exactly where this person is used as seen
Ah a lot of possibilities when as I
said could be on to something like object
detection in any industry is hasn't been like
comparison So no worries Our government is using
this you know to compare the majors find
a lot of things So this is usually
used by Interpol FBI We're seeing movies also
really compared to images and try to find
out what is the best match after they
they are six Heard application could be in
the retail industry There they noticed that is
ah run up which is displayed on the
website And there are a lot of I
did use that fast road Tennessee There is
a full So does the lamb is that
there are certain interviews which need to come
from them technically possible But there certainly was
which you don't need to manage it I
like color shape size What it is which
company That belongs to a lot of things
So those things which can be easily scanned
and put their automated So some of my
friends working with Amazon guard Reliant Strains and
all this that's what they do in the
end Hardcore and do temper division on Do
this growth What is objects So whatever the
scene deported So for example that is a
T shirt we just displayed Let us or
something like that They can find out It's
a V neck T shirt Is this color
this This could be a problem Size Yeah
So some application final when I will say
is on the leading enforcement Jelani Google car
who will glasses A lot of examples indicate
from this Yes So it's a combination off
comfort division plus reinforcement Learning where What is
reinforcement Learning It takes an image and lancet
It does not have to do all this
if it is not in the car was
it You're not rejected It should try to
understand waters Good Okay so this has applications
Any questions under those Anybody wants to know
anything more on the applications specific to your
industry where you can use it Do let
me know So Krishna have you come across
any such a usage in terms off a
charge Services are paid all services industry Uh
yes yes yes yes I did not say
that The only payroll but yeah one off
My seniors has worked on the window Me
I'm not sure how 11 returns with your
So that said it All of invoices process
them Yeah So from these invoices they try
to pull out the numbers there That might
be You can do something on who you
are It is even in the not sure
you're So we have some kind of requirement
right now in our company but I don't
think it's assess fully off the capability But
it goes like this from the Soviet new
clients Right When you when it comes to
implementation Well rather than going to plant asking
them questions about how the current system is
designed and watch components speed on components Do
they have we actually stand the employees and
pulpy slips And from that we could deter
mined that These are the components that are
there and how they get calculated The formula
and everything gets still I without that can
you Oh no it could It was a
failure earlier but if there is something that
you have seen from your market industry or
someone off your friends Yeah let me show
you right now Yeah you could help you
Okay I'm not sure exactly what you're looking
for I don't under one off these there
We try to float certain bags or requirements
are off a document So it was something
similar to your thing You can say Just
give me a minute I'm not sure the
whole case study because it Zaveri it's a
part of your next to Nick's So the
next morning anyway But to give me one
minute toe Modern number 10 and B I
just finished the score So good time received
If you observe year I'm able to bag
certain words off this Yeah so you can
In your application I would say you can
use partly to identify the text partly as
a text processing And whenever you find a
formula or a process or some something which
has what functions you know you can use
and help me to find out which which
is the closest it belongs to before that
you need to have a very good core
person Corpus is back India You need to
have all possible formulas I hope that's what
I'm talking in hand in sync with you
What I was trying to understand this Is
there any way Well we can have a
mixed kind of database having images as well
as uh the raw data that we get
Where Which we were looking into much England
No So you have to separate purposes One
is for next one is for you Okay
maybe we should be able to board a
student Yeah definitely Definitely If you want to
walk understand Do me a favor Give me
some Sample it I'll not expect the whole
data from your organization Give me some draft
fake later which is in sync with that
way Smarter problem Okay I have done something
Wonder text purely But yes you can include
the images and we can combine both of
them I think so What I do currently
is I have done one of these implementations
where bees can left piece Yeah and we
tried to pull out requirements from RFP using
the image process so we can start using
text processing and you can say which requirement
is a function of requirement which requirement is
a non functional equivalent which is a business
requirement under functional requirements What possible They're tapping
on one other docking more automation another no
functional docking unit Whatever You know it's very
easy to pull this stuff The only thing
is the lucky I got a good car
first for this Yeah so you can do
that All right Okay It's a shutdown from
You want guys Just he start this Okay
This is this is one thing Anyone else
Everything is good Yeah All right So let's
see what we can for for this plan
for this particular station is we'll take an
image and data structures and diets will see
to it What are they convolution for Dust
Comments on an image conclusion Una Douglas is
fully collected Nearly little world convolution alias average
Max pulling sealers and forward and back cropping
Seeing Yes Good So I think we're almost
everything Now let's see what type off images
are What it sells can consist basically so
we can have great fun we can have
on rgb r p Bacar grayscale if I
give you an example What do you mean
by Cleese Windows in here The lowest possible
reliever picks seller to say zero and highest
possible Could be 2 55 Foreign zero could
correspond to a white to 50 undesirable correspond
of black to 50 If I could correspond
a PR right And whatever numbers you seen
middle will be there different shades that you
are seeing here Good Everybody gets this Yeah
Okay so now moving on What about this
one He had also seemed stuff Here we
have three different channels When it's called Rarely
one it's going green when it's Carter Nothing
Ask scribbling Red green And what are the
numbers of year CMGI zero could be darkest
Green to 55 would be lightest Yeah different
shades of being you can observe over here
Zero would be lowest blue to drift If
I could be I guess zero Now you
may ask me if you find and how
the white mixed match How do I get
one color story for this Purely green The
indicator from here war effort is a combination
of all three of them In that case
there is a special function which will give
us the calculation between all three and give
us wonder But we shall correspond to one
off the color matrices which is stored in
our computer Yeah so if you remember that
is ah special type of plotting function gold
I am sure Yeah in that You put
any medics off your choice Say creator 64
64 matrix off your drive No problem You
put it and kind of plotter teasing I
am It will definitely give you some or
other color So what does it mean is
there are some pretty defined values stored in
the computer We fix up these values Okay
So this is where those two things which
we deliver it next morning on what images
we can see So it worked Rudy Anti
meters is where is the to the metrics
that Rudy metrics is your and costs and
in order to clear the mix So I
will say grayscale image could be a 30
meters and the RGB will be treated So
in that case I will say 42 across
30 through I sleep like music is a
three channels I was talking to image and
if you want to make it 40 I
would say if you remember in your mortar
we have 42,000 He made a study Do
cross 30 And that is the 40 But
don't Don't take it over the 40 unites
began We can ignore the first and the
last part 30 Doing a study does what
we're interested in Yeah And please remember pixel
is the lowest form in ritual image could
be presented It depends on us how we're
sampling it If you want more samples That
means if you remain 16 megapixels it has
more information It is more sharp If the
same image has went Make up excel even
understand how blood invaded will be if you
want to see how it looks like a
very lonely sample The mess Look at dusters
Our looks like each square we hear presents
One picture It would have done it in
very simple bills each dot on the equity
present one picks up In that case what
would happen We could have easily being able
to seize eyes nose mouth and beard Look
at the bloodiness Okay So if you look
at this this is how any means Look
Flake on the package This is your friend
This is how we give numbers to this
familiar with black zero Anything which towards white
is 2 55 You can check it out
Look at this than you And look at
the darkest around you Is it looking Deciding
perfectly I think And then you remember whatever
does And this is how it looks like
One over uh bite since eight Back in
part off Okay What if I wonder Normalized
this I'm not happy with zero What happens
base In that case what we do is
really write it with the highest What will
be the change would be made range now
and somebody spend it up If I do
I'd if I normalized this what should be
a niche between dealing This is how digital
images processed in the back in part of
it Now if you want to understand how
it free their works very simple Just look
at one particular corner of this image is
being scanned or multiplied by the Emperor filter
This is my friend That and minutes of
the filtering finishes This is what we get
out off So a three cross three is
becoming one number So I can say I
have divided the whole this thing by name
is totally nine Meet numbers are represented by
one This is what does my and how
does it happen if you observe here medicine
minus one into three plus zero Indo zero
plus one into one like that You wrote
for all the rows and columns at the
end we'll end up with my industry some
number four minus If you ask me what
is this number represent I would say it's
just a packet So when I pick up
one feature out often image the answer for
that pattern or that image Good minus So
tomorrow when you get a similar we get
almost all the number similar around the lake
to another image You can see that this
imagine this is perfectly safe Future Okay so
going on now there is a thin line
of difference between convolution Correlation So there's no
waiting over Okay well that's the way Decide
this week again I was a field that
is that This is some randomly chosen three
Crossley matrix here for example So what are
the favorite relation Convolution Kids in convolution What
I Lewis it is in correlation Also we
might be plying and coalition also emerged only
different yet is every time you want to
use the fact that no you flip there
you flip them You don't use the same
filled out every day Give flipping Now what
do I mean by this Let me show
you an example or this Let me start
my color Yeah man you know Okay so
let me take you to that place Yeah
So let us see This is when he
makes given to us Okay These other filters
that I'm willing to multiply this image mint
So look what Have a close look at
this particular figure This is how it looks
like Well you see what I what I
do So when I change I true guys
How do I play around with his numbers
What I'm doing is both my extreme vertical
enzyme making it minus one minus one minus
one And whatever middle values that there and
keeping it 11 Look at the original image
Let me run this At least I need
to understand Yeah Now let's strike up blood
This my originally Mitch I have changed my
filters earlier If you saw there was a
different filter it was vertical stuff Now it
is going to become Did you observe these
days Yeah Just by changing certain values in
and out I changed my complete image Howard
is interpreted You get this Yeah So this
by changing this freed us I'm converting this
image into this image One more thing Also
legacy I divide this by C 40 for
example Somewhere number It would be blood Yeah
When the twins and minus one minus one
minus one and minus one Let's tryto 100
This Yeah And what if I remove this
for example Yeah complete images change So please
remember when you felt that changes Definitely The
value off epic search inside the Matrix also
changes And because of that you will get
different images So this is how we play
around with committees And is that this is
how What What What do you mean by
freed Us Good Now let's seeing this one
You people see the implementation in the weekly
with you under this or you have to
do it Do you want me to do
this Image processing musics Are we know this
No we don't know this most book Lex
See ladies imposing We have seen this snap
chart And uh what else we have We
have God instagram and Facebook and a lot
of them Now you know if you just
look at it there will be glasses put
on your face or there will be some
captured on your face or beard A lot
of things happening And also sometimes they try
to change the color off the image How
they do this very simple Only they have
to do is they resigned One filter like
this That's okay So this is an example
of a simple filled you can imagine There
present a little bit bigger for such a
be there They change certain bag learning a
note and they try to understand that this
is your face Remaining things is not okay
So if I multiply this image with this
thing we're will to get this Now what
is the pattern here If I multiply this
never sharp on any Mitch this is I'm
running towards shot money You're highlighting the hidden
features only This is what I call it
sharply If you want a toe edge detection
This is how you are going to do
so You will see only edges over here
Okay But where do you think we need
this days Despite it through the border Except
this Snapchat in a lazy does Apart from
that anyone else you are able to realize
we need this of medicine Okay Perfect Anybody
want to amend an image in your corpus
You can use this Good one Anything else
Sending is this Okay let us say we're
talking about something about in the medical industry
For example in medical industry leaders say you
have a snap short on a microscopic image
Off persons sits and they want to predict
where that these are cancer cells Anonymous is
in that case What we do is we
pass it through one of these filters particularly
get it just like this in trumps edges
me You know it might come from how
big is the size of a city That
hosts would be one possibility and I've seen
extensively Health scare using this marries now it
is even idea sophisticated hospitals that we go
to it can Snow Nieto showed uh excellent
with a doctor except machine itself tax You
see this is an accident Okay This is
an ex Elsom are after body basically packed
itself Where is the problem It ordered it
Fix the problem How By this kind of
machine By this kind of competition Okay also
I would say not only computer vision you
can use even our again and mystery months
over here to identify the sting Moving on
predicting Letna really famous morals Or when When
I was starting my engineering all of my
next book had this image The name is
used years on your Yes What is this
taken image for different or what is this
foot up sharpening figure Now Is this filter
in sync with our earlier For a look
at this for the race Zero minus 10
Look at our leaders happening for you know
minus 10 minus one Fire one Okay so
wherever you go this well that is going
to be same Now what if I want
o sharpen my image more What is a
possibility would have been changing Anybody's even begin
to imagine five years good of it So
just understand the pattern You will get the
processes take this example age detection Now this
is prediction Locate this and look at our
age detection here Is it dancing Not at
all What does it mean It is not
necessary always to follow the same Yeah in
this age of addiction What we have done
this We have highlighted the extreme edges with
zeros and ones and we don't be a
changing into mines Okay saving If you want
more sharpen it What we are doing we
are converting the complete third rule Toe this
zeros to ones and it's too here also
zeros accounted for minus one minus one And
this one is in the middle of all
zeros In that case you get this happening
is like this is also Chablis Moshe I
want to go One more level about I
could have done minus two for minus 2000
to for it was us better when one
was about the shock Us Okay Perfect So
this is what majorly in the north We
do want to image processing freedom How you
may ask Ukip will find radio We do
this We did this because tomorrow you need
to augment your remains Or tomorrow you would
do you know basically compared to images And
let us say one of the images in
some form and everyone isn't something What you
do is you apply a single filter on
what So that you bring them to a
single level and then you physically compare them
all You use distance for me I don't
understand how far other points Okay good Perfect
So this was your image processing Let me
give you an example on this Let us
see This is money made off meat Seriously
this is one more image which I have
taken So this was usually you know if
you look at our I d cards I
don't know how many off you feel like
I have Ah six year old I D
card from Gap Germany Okay if you look
at my imagery for six years and if
you look at my image now this is
something like before enough something had happened to
me because of which this image isn't nothing
with what I can only look like Okay
so let us say this is my or
leverage and this is my current So that
is it is something on my idea There's
something which live My organization is taken Now
I want to see this is ordered and
this is coming And that could be one
morning makes Trump wasn't looks almost similar to
Simon See it This is a positive images
and negative foster in the center of this
kind of me Negative means I know it
is not What if I take this three
separately through a convolution Your letter C What
if I get this person through convolution Literally
Look I will get a set off mate
It's agree I take this image and get
a set of matrix I take this English
I will get a set off majors Yeah
Now what should I do to find out
that this to our same Where this to
our north sea I need some fact That's
right I need some something Some function for
recording Do you could be used here Facial
features Yeah but if you observe here these
are nothing but numbers Is that just number
Some meters Is So what What a lot
of them on when I use it You
remember Vienna What else can Yeah you know
what does CNN doot distance Yeah I find
out the distance between these two meters one
Yeah What if I try to find out
the distance between these two Okay let us
say that descended in these tools Alfa And
what if I try to find out the
distance between these two Let us Sabina Now
tell me the relationship between I'll find place
the distance Uh when I subject to similar
things what will be Al Franken s attending
Cozy Yeah When I subtract do this similar
ending So can I say I'll find always
left then Yeah orders that Can I feel
like this far minus B Tha will be
always less than less than less unequal toe
Does it come to your Okay fine Let
me not confuse the ways What I was
trying to say is please remember at the
end of the day these anything but numbers
you can use any off your must relearning
classifications techniques to do this here What I
did was here I used lost function as
distance We can definitely find out the complete
distance and begin savage to are nearer which
to our father So if I say if
our doing majors if we saved women is
a similar I will say let us say
the similarity should be around 0.5 So we
have subtracted them No And they say I
got a value off 0.29 That means the
difference between them this point which is very
less discomfort A point I can definitely say
that that this sense is very less between
them But let us say when I compared
this with this I got a distance of
points of it is about my threshold So
I can say that Yes both of the
you can use even CNN in tow Back
level very simplistically You need to go and
do all hi fi stuff on this This
could be asked him Blessed This is quite
matricular I don't see it Okay so this
is where you will need all this image
processing skills sometimes to another data I hope
everybody is in sync with me You know
amid Shatti a gosh through hurry score she
frontier a ghoulish good Yeah Perfect So now
let's see this case study where I'll say
it's not immediately a case study badia a
Some of the basic concepts which will help
you toe to work on this So what
do I have I have got number I
have got a skin image which has a
date us And there are some images restored
And also it will help us to do
some kind off io functions Okay Next there
are some priest redefined data sets we just
toward inside biting So inside basically ask image
One of them is coins data So I
just have toe call the data coin state
and push it on toe image and find
the type off It's so huge Really doesn't
number area Because if you remember I just
said images are nothing but come made exhaust
numbers Okay And if you want to see
how they look like First of all if
you're in the Shipman's I love word to
Yemen Three Cross 3 84 That image One
image This is how it looks like So
if you want to play around whether if
you want to see what is he made
00 has 47 numbers in Jordan Yeah So
this is nothing more Your 47 Also if
you want Oh bring the top save print
stuff from 101 50 on Excite and from
my side you bring some strong 25th to
the 17th reading If you do that and
you printed out You get an image like
this which is one of these Ah what
is here Aucoin's Okay No see Map defines
what type of image it is If you
want a grayscale image and put a grayscale
here if you want a red green blue
We have defined reds greens and blues have
shown down the lane What is Let's do
a 20 minute Okay so this is my
grayscale Okay Ah this is my m A
sort of the safe He says you know
one wondering Three se zero comma Seven comma
One for examples 1 30 Okay so what
do we do now Is not the same
one for sure You would get a scanned
image so angry In that case I'll get
agree one If you want a green one
greens we'll get a greener Yeah So this
is a manipulations on Seaman later on There
is one more set off image court date
or coffee so that it looks like now
look at the dimension of it 400 cross
600 cross three That means it is RGB
kind off channel Okay Next is if you
want to know focus more on do the
You know the red part of it So
what I do is keep the third channel
as it is normal And I Syrians So
for example if I say greens here Okay
it is going to give me a normal
image when it is green Howard Looks like
that's what I'm just changing the finger Why
Because I'm not imported the image here Okay
If I want to make it more greener
if I want to focus more on the
green part of it So what do I
do Say I changed the skin so if
I want a sharp when it basically from
the green side So when I printed Look
at this If I want to make it
more green So internal blue I say greens
Yeah It's gonna be more sharper and sharper
So this is how you can play around
with some of these numbers Say I want
everything See I'm not sure I never tried
this Let me try Do from 123 c
Do you never know Yeah Yeah Good It's
not play around with this Yeah So this
could be some some operations You could use
it next Is understanding the freedoms that we
have short So this is one new image
and this particular email If you observe you
want horizontal Ah bus So this is the
same image Is this The only thing is
we're what afraid out here If you want
to sample it or if you want those
edge detected horizontally the first in the last
one or is under one should be minus
wins Other one could be 01 nor a
problem if you want to do the same
operation vertically Common sense The first and the
last column should be minus wants either on
should be much okay What are we doing
Basically there might be playing this with our
image That next is if you want to
sharpen the study if you're on the edge
protected properly So if you go to our
ppd the same filled a little use for
the camel minus and minus on MSM and
instead of it sort of safe for you
to 16 here for example Yeah we have
brought back the original image Almost What if
I really use it to Yeah so at
it perfectly It detects all the images and
it does not show in black and white
You can easily see there's and Tedy kind
off Cuba's over here Yeah Ah seconding is
if you want to blood and image So
if this is my original image And if
you want to blood like this where you
could lose your toe multiply are you can
see you have tow divide something by 16
and multiply within identity matrix All I will
say And Aarti 1 16 You can take
any number here Say when my Fife definitely
You're gonna get a bloody May discomfort to
this As you keep going higher and higher
air your blood This is going to wind
up already Yeah Good So this is what
it is majorly onto your teammates processing But
if it play around with images import your
own image and try to Blair on with
this very personal you will get to know
Now one quick question is sometimes we're using
four cross four filter sometimes every year using
three crossed in Frida Okay Do not hesitate
to use emanate Cross it Nothing wrong and
do it The only thing is you have
to know everyone What kindof feature you want
out off it that you know Ah there
could be many websites where these standard fritters
are designed So you can download some of
these fritters and try to create your own
values out of this committee's play around If
you know never to find them on Web
Do it We know I tried to such
for Yeah Good So get one image I
understand what it is moving on So something
complex now So let's see how to do
all this now how to go ahead and
can do convolution out of this So these
are These are the music's Yeah since observe
the animation that is happening on the right
hand side So on the left we have
got three bigger images Where were concluding them
layer by layer So the 1st 1 was
Lian one Now we're doing layer to convolution
and finally will observe the green colored sense
These are the final output off your images
Okay And also you observe the right inside
how rgb is walking on board Okay so
any mitz is nothing but a combination of
ours Ian beat three layer and finally we're
converting it and converting them into one particular
value So here Forecasts for is getting converted
One particular work So we have contributing the
image Okay Good Now moving on another simplistic
version where you don't need to focus on
all of them So if EUobserver in our
previous animation we were going sell by six
So if you see we did not move
anything extra we're going to almost all the
sex Did You will see that Yeah Here
And what are we doing We're not going
to all this Is little wonder some focused
Yes for example Yeah Now how do we
know that there is some factor called stride
If you guys are senior legally videos The
professor's talks about something called Stride factor What
is strange medicine But if I have this
say four costs for image This is your
beautiful crystal for cross for image And if
I have invigorated one color if I say
I have a new cross true filter this
will be my first food If I say
my stride is equal to zero that means
I have to immediately go to the next
But if I see my stride is they
were toe So what I will do is
listen This waas Initially it was intended like
this Now if I say my strangers to
I will skip one and so I wouldn't
go to the next one So in this
case it's not possible is not dead But
what We'll do it Just imagine those of
more corner I wouldn't skip to cells and
more into the next one So these are
examples where you can increase the stride and
focus only under certain patches off your image
You don't have to worry about the whole
Okay good So this was your way than
even Still this is basics of neighbors giving
me uncomfortable Now comes the real part Holder
decided number off $3 this size and that
size So please remember you see two formulas
here when it's called output which one is
called output height And as I said we'll
go into reverse direction Okay So first thing
is what output you need from your convolution
Lee must question So let us see from
this whole image If I just focus on
this tires on legal status door it will
be enough for me to identify that this
is a bus Now that's not a problem
here The problem here is how to decide
what fate aside I should take about So
the first thing is I want Oh say
I want my I want to understand what
will be output So what do you guys
do Is either you fix it on this
or you fixate on this Whichever way you
like it you can We cannot have both
options Say I fix it on my say
For example I used to cross to fritters
four number of tanks for filters My stride
is equal to do That means I would
skip Toto cells and more I don't want
to do a very good Sampley Yeah for
that what I would lose I'll take the
rent of my image which is 32 Okay
mine is ripped off my filter which have
decided as to so 30 to minus two
So let me do it properly Do you
know minus 21 minute So I reduce the
stick necessary that we would rate We're test
32 What is this Red with my image
Okay a mine stripped off my freedom That
is to plus two into paddy batting in
the sense sometimes the image is not enough
to scan because off our feet that we
just saw When Just know when I was
trying to do stride do it rained out
So for that reason what would review departed
bad means add an extra layer of zeros
around it He never case We don't need
padding So this will be zero I will
say to Indo zeros You know they were
in my stride What is my stride Stride
is it will do if you compute This
30 to minus two is 30 30 by
two is 15 15 plus minutes 16 This
is how I got my convolution now why
What I did was I froze this and
a gorgeous What if you freeze this and
you can get this How sled is simple
You say 16 over here And don't put
anything here So I The question of the
16 is equal to 30 to minus filter
it plus two Indo zero The well advice
tried I said us to hey plus one
take everything on the other side You would
get defender it So it will be 16
in to do is 32 hardly do is
equal dough 32 miners illiterate plus one So
I will set 33 e I don't think
that Okay now it took it Yeah The
32 plus two is 34 So when 34
goes this side become minus 34 That will
be minus two is equal to minus off
efforts number two minus minus cancels I will
save my filter where it is nothing but
which is nothing but your answer So whichever
way you do it I'm OK It Yeah
you can check it over Does this quickly
check it up Isn't matching here I would
not do it people computer But the ways
just take it out isn't matching Are you
going to do it Padding is zero in
this case OK so don't take fighting factor
You mention 19 Right Good So what if
I would have taken three Cross three that
Can somebody tell me that would put a
layer with would treat grocery cleaner here until
have to cross to let us have taken
three close three as a friend Now what
could be more part of it Will you
need padding floors Yeah get up So our
numbers can't compute Yeah Okay 32 Cross 32
Steuby Let let me not confuse you But
let us say this is uh Let me
put some some some brackets leave See a
one se do clean four and five OK
similar to that one Do three foreign faith
Good No What should be me Filter size
Such a really That orders I will give
the front aside Let us say three cross
three is my freedom size Can you people
tell me the output form when I would
put a legit What Crossword Use that formula
and you're fine It's just taking on it
regardless Will be ableto visualize the same thing
in your convolution ALS the yoga put hyper
parameters there watching with a torch Five minus
three plus one No pm Find mine of
three Okay Plus one batting Should be one
No no zero Right So find ministry derided
by two So that is to buy Do
that one I Okay What do you do
that I want them close in us Skintight
We don't notice me on I'll put the
formula for your output Today is equal to
that image It is a lot of a
lot of nice XP Zmuda still arise When
When it was that Yeah Okay so ripped
off The image minus filled out of it
Plus two times padding divided by stride factor
plus one This is the formula for our
put return Are good height with the same
form Now tell me guys what will you
do You have to decide on padding You
ever decide on stride How will you do
it straight Should be run Slide should be
doing what it should be Yep that is
the ruins Trials Do they output is to
grow through Okay so one answer A goddess
to cross to Okay Perfect So did you
see five Crossfire image Now we've got the
best features out us to cross to Okay
Next Anybody else changed the batting and strah
straight So you don't Today's what worked What
here Drew said Waas He doesn't want to
do in East change in the body and
also doesn't want it destroyed So sorry Striders
were truth to rate in that you can
say it's this one and it's quite is
21 Then in the next layer When you
come like this of the and come like
this so do people agreed to do grass
to perfect one 234 Because he has taken
strides Toe What if this trade was not
In that case what would be the answer
It is a strange one Three grassroots three
cross tree Exactly So you will get more
features Three Grocery What if you take a
body through where there was a parting their
legacy and ready e started If okay now
I hope you're able to get it is
not always necessary That was bad Or don't
have more strikes and all It all depends
on your filter So you base line one
off them I don't You baseline this or
bass lines and then choose that There are
not I would always say b is lying
this easy way Okay Is everybody in sync
with me Already Got those guys from next
week will not be able to discuss all
this OK so when I say something I've
taken and according people people should be able
to identify what is uh strayed or dispatching
Color of deaconess decision Okay Yeah Mine of
Yes Anything else Save any anybody wanted to
repeat No just spreading it One done work
with the baby effect on actual image See
if padding is one through padding is like
putting zeros So you will get one extra
white colored pattern so that it's I will
say patting person is not good but sometimes
already medicine off some ord size No to
match up the fadeaway of group body Ok
OK yeah It is like a say quite
colored layer No I don't really based on
that So Krishna biding would be would be
used if they aim it is not like
in squared and she for something Karen Correct
Agreed So here we have taken five cross
five Now whatever Those five cross six So
let's Lex tried about And guys remember it's
very difficult to visualize all this so don't
strain yourself If you say ok I'm not
getting definitely one short will not be able
to get it Say this is one Do
three your four and five Okay now if
this was five Cross say three for example
one to entry Okay Now my intention here
is to use a three cross three filter
Is it possible First of all just take
it out days a three cross defender Howard
look like So let me increase the thickness
So it will be three Cross three Yeah
And if I say straight is equal to
one now what's gonna happen tohave One more
three Cross street Do I have space No
So in that case would have to do
after out of one extra padding layer on
the whole image Okay So usually you will
see in all the courts is usually by
default The take a painting off one or
two It's OK because we're not sure something
we won't be able to visualize this So
it's OK to take an extra padding Doesn't
matter Sometimes we don't do it if we
don't do it If it is an issue
it will throw another Anyway it'll notify So
once it notifies us either we change the
size of filter or we change the side
off Good Michelle Zucchino Yes What It Okay
so moving on now So this is very
important Please remember when I share this keep
it to the duende is very handy Also
it's not a big deal Okay so this
is gorgeous Patty What It Lewis It'll and
wondered Lee adding extra number usually the zeros
So It will not impact us much if
we do poorly Because if you remember what
is pulling does this building will pull out
the best features If I pull let us
say sorry if I pull let us out
of this anyway 20 is going to be
in order somehow Okay So it didn't affect
much but at least it long in Dallas
Strong Uh doing those So Yeah Next us
willingly So we have to type of pulling
off level I will not say that Said
you can have your own version of cooling
also but these are the most popular wants
Max Fooling our is fully so average in
the sense you take average If all four
of them and you pull as one number
Max is take that max effort So this
gets everyone window Okay good Yeah Uh days
on morning study This is the max poured
image from here This card So if you
see 20 Bacardi 37 11 this is a
record If you still want to Maxwell this
for that then answer is on one together
Did not see that I'm sorry This is
coming from here Okay This Also if you
observe average pulling so they can average off
the 1st 1 Let's check whether it's and
think 20 here Soto 30 to 40 50
to 50 to wear for golden study on
it So what does that conclude Anybody Any
idea we can use any other country relation
You've been pulling toe haven't since fiddling Max
e here Exactly Best features me feeling You're
saying we'll accept Betsy and they were playing
out Matina But yes you don't spoil any
mission If you want to spoil talking part
of an image as a blur part of
something yeah you can take that mean values
Good one Anything else out of this and
we didn't need remember way can take a
kind of maximum value minus the mean Okay
sangria And in this case it could be
uh minus Yeah but you have to design
your own At least it will not be
present in the standard of functions of design
Your own function standard values that these two
next fooling on average Good So that Sirte
Yeah So now how it looks like so
fully connected layers So what we have we
have got your input Is 11 Q Posten
Yeah So where do you use Max Pulling
versus Have let him in Are you okay
See you Zmax pulling onto Let's say you
have a low low a sample image So
let us see The sampling here is to
make our picks and for example and assembling
radiators 16 main topics In that case what's
gonna happen It's the images which are nearby
No we almost similar It's so in that
case would be digging average not abroad But
if you feel the sampling off your original
image is not that great you do maximally
except the best out of the street Okay
see I'll tell you why Let let's let's
go back Are you are saying that if
they made this high resolution can then go
for average operates on different is no other
solution See if you look at this one
okay This is only to stew zero or
one writes in black and white So now
if I have a filter which is focused
on this ritual you think is off other
importance days Max that way you know where
there is a black Definitely gonna get this
So this for to cross stream age now
is resented by one which is quite black
It is nothing but black in now Let
us take one more example Say for example
you were having your stuff in this No
What is the maximum number of Ian All
of them are once So if you do
Max pulling wonder that What do we get
One But if you do let us say
average under that What do we get But
it just three by human form You know
this I didn't know this So anyway downs
that is We got to know that majority
of the features over here and I don't
want Yeah we know So this is some
example North average This is an example of
Max So I think about the differences If
you are rich pulling then especially where for
example you have full for black I think
that difference between the different values would be
would be more of a range Okay Would
it stick Let's taken example Say where do
we get for here We don't have it
So not to say we Maybe something like
this Yeah We take something like this Three
Cross three Yeah So if you take an
average of this year's I agree with you
I know FIA works out So we have
got 12345 five directed by night That will
be our average Yeah correct Ok good So
it raises your disability Is trying to say
is trying to say that you will exactly
get the number How many blacks are there
How many rates by many So that are
denominated Nothing But your total number off waits
without me So that is my ass In
a sense you're getting more information right into
the next layer or in the next character
Okay so you are basically taking the best
information out of this and moving right here
you know get the best information Me black
was information means weight That's you don't want
to wait Don't do this Now you get
the point Why We need support for just
one We just need the best features We
don't need extend background stuff from this image
It's not Do it good So this is
one radio represent image If you want a
little bit more simpler version you can do
this What I feel this image is a
better representation off there You can take out
certain features out often Imagine medically classified that
it's God going This is this could be
a real We will see these little little
later When of course only the ones can
Everything certainly just will make it up completely
Okay good So this is an example I
will say off our CNN reasonable convolution Your
networks you're in see down the way this
comes in that fourth paradigm for it to
be good So we have done almost everything
which was needed So next session what I'm
a lawyer that we will start with implementation
off the CNN And then we'll want toe
a very simple top The call transferred learning
So just to give you guys a very
clear quick find its idea into next week's
radios Don't body those vdot elects next flex
net then imagine eight resonate all those nets
that are produced No they're produced by some
people which you have done Lord off complementation
combinations or find out the best combinations of
convolutions here You know what it does it
basically whatever image you give no tries to
classify you problem now What has happened since
these Your networks have one certain competitions Their
standardized What does it mean Their rates are
stored in an H Lee a former now
on what you can do is you can
build up your cord You can download their
weights You can really the architectural for them
You can automatically bound or their rates and
you can build your own FC it in
a day So whatever your normal or no
that will be only convolution mules then lastly
at the last 3 to 4 layers that
were supposed to do our own list That
is your funnily fully content in your little
clip is what your design that's your network
is ready You don't have to take up
paying off building your own scene There's a
school transformed Learning that's all is a topic
for next week Yeah way down See it
plays You cannot find them You can train
this today and you do You are fully
connected Neural net When you play in your
model these are known trainable static stuff This
will be order enables that Do you think
our problem is we sold Yes right You
don't have to take a pain off designing
a very complicated neuro network You know network
is good I get it But you don't
have to take a pain of resigning All
this You are getting this ready Made this
part this one What you are supposed releasing
A new people are very good at this
now Got it This car plans for money
It's like we're not your design Accord gentry
core and give it all of us We
just seemed the import duty time Run it
as a classifications More That's it So don't
get confuses The professor is showing elex senate
election It is very big You just have
to understand that this is the ready cooked
ready stuff You're importing it and running it
on our morning so that I don't have
to go through a pane off training my
own more And usually the perimeters are something
like six million 10 million 16 million That's
the reason we don't need to train you
get Can you explain with an example I
mean that um So you see that maybe
you have a corpus of acres right You
you would get find off a model which
is which already can identify vehicles because it
does learn because going to look or person
Then you just had maybe a few more
less on that just to look at Maybe
if you if your problem is trying to
defy afford maker than you work on that
he's not a can't got it See we
should have you heard about framework anytime in
yes Industry Yeah what's a framework Framework is
basically like a date line off Oh okay
So for example let us say you are
doing Levi's shelter Okay let's say Have you
heard about a tree It We know it's
implementations under lean It's a frame book Let's
say you have a service and you want
to improvise a service So what do we
have We have current scenario to say What
is this services Current sedan You What is
the problem You are facing their how you
intend to solve it And what is your
timeline to solve It later said there is
one framework available to us What do you
need to do when I get the stream
up to you All you have to do
is your to put in your deed Yeah
could something like this You can imagine that
There is a ah neural network which has
its own corpus which was trained Longman which
was approved by Lord of Adidas And this
long which kind off is similar to what
you are doing here To save the corpus
over here was large off image or object
identification Any guy I know it could be
any different Now We already have this new
element O'Grady So what these people have done
is they are actually frozen there waits for
us and converted them into a niche Dear
former what do I do I take this
creates as our framework And now I just
put in my knee down to that okay
and used it I'll give you some of
the popular Vince which I am comfortable using
his re Jiji Nick Mobile unit So what
is Nobody let you use it every day
on your phone If you people observe your
foreign back to your face it is not
possible to put a ready every stuff on
your from your people of computation So we're
somewhat simplified Region toe a very light with
one called mobile It and we are using
Does it Anyway you can use Ah imagine
it You can use the rest Nick Rds
net We will see down the line water
the sticks But in the next videos you're
going to see this Okay good So when
you next week should be very simple So
once you have got the concept of printers
and convolutions next week should not be a
tough one All right Week one I think
is that the press and me like 56
I feel is a model of physics are
okay Alright guys So I think I'm done
If you have any questions do let me
know else Little asked question on the project
because I think I when I was looking
at the there were some missing values So
for neural networks do we need to do
any imputed soon Missing Majesty I think there
was something also that was provided in one
off the case studies I guess if it
was that project through this project that is
now missing Okay Before moving on I just
wantedto ask you once and building Let us
see I haven't image but you bitches see
Ah 64 cross 64 Onda over here I
have ah newly network say the newly brokers
having 500 well inputs for example Okay And
I will say again they have their own
hidden lives And finally we are converging This
your leg Look to say one classifier over
There are two classes speed on kids So
the point over here is we are supposed
to import 500 Well inputs my current input
The 64 or 64 cross say it's a
grayscale limit So one can you guys head
me to put how many convolutions I need
And what will be the size off my
Fritos Max pulling and all that Can we
do this quickly so that I get to
know that you people are in sync and
we can go We can go for the
runners So let's let's start putting the blocks
over here one by one Yeah So let's
put the first block So what I lose
I will We will write the filter size
over here um filter size and Max fully
and finally will feel well put here What
does What is the total number off You
know they have got off Yeah So let's
start how to do this How to design
this freighter Three crossed riveters Okay so we'll
start with three Cross street okay And how
many I need What exactly On 64 So
do decide on that No Yes So what
From my status this particular time We have
no clue Oneto how many we need And
also for now you keep it empty It's
OK but at least let us say you're
decided Three cross tree So if I have
three cross three here What will be my
output here So what would be that would
put off me Uh what do you see
Friend doesn't leave There's 64 Blessed three minus
one sugar Yeah Okay What is the formula
ripped off The image minus twit Off the
filter Plus twice off padding divided by stride
plus one This is the formula OK Yeah
So now start putting it So first of
all I will say Okay Stride is one
only I'm not changing My straight and abiding
have not that zero party so wouldn't be
the size easier So 64 minus three bless
one 64 minus three is 61 61 61
by one So 62 So I can say
that our court that I'm going to get
will be 62 across 62 Now if you
look at this and if you look at
this it does not make much sense Toe
Why Because we have not reduced much So
do reach to fire one toe The 500
means when I multiply these two I should
get firing somehow So if it might apply
64 64 you are going to get 40
Nance's Yeah So this particular thing is not
going to help us In that case what
do I say is this transformation is not
that great not to reduce it What I
will lose that I increase the size of
my furniture It's not fair or else I
will increase my strength So let us say
increase my strike before a bit Now can
somebody tell me sweets are number Do you
like what is Find that use 64 minus
three Orosz Let's let's let's be fair Okay
64 minus two to cross to will not
broken the 64 minus two That is 60
to 62 by four That will be 31
by So that is 15.5 So say 15
Will date 15 plus 1 16 Cross 16
beginning So that is a good transformation So
now what is The problem here dies is
this is not an easy task So on
purpose I took these numbers so that it
is not an easy task that okay I
have these things in my mind I have
some import Let's try to annoy the input
or something And also it is not easy
to decide how much is the size of
the filter What will be my partying What
little So it is good to know the
formulas and do convolution on the simple images
But when you go on to larger image
it's a one megapixel image for example And
if I ask you about is that okay
My my images one Makeup Excel Can you
design a car CNN for me which which
would indicate fire one do over here So
it'll be you can do it not a
problem You will be able to put one
by one All the filters are is even
you'll back propagate and try to change a
good thing Nothing wrong but it'll take a
lot off So to avoid these confusions What
we do is we use a concept off
machine learning Sorry Concept off transferred learning I'm
sorry Now what we do in transfer learning
is let's try toe See that Can any
off your name One library from machine learning
which is the best like which which has
almost all that Governments are all the functions
that we dropped like I'll exam you an
example of SBC or canon or Decision tree
or in the forest Which library we used
for I second let us say we use
that library to do that Now what is
exactly secular kind We program s we see
using foreign If loops We can easily do
that Why don't we do it Why Because
they already have a function Somebody's return erred
Somebody escorted intelligent requests is that it will
help us to input our unique data And
according to that you get a customized out
It is solving my purpose So what we
do We call this now when we call
this particular thing Whatever doing we are getting
the stuff ready made from them We're getting
the hardware ready from them The only thing
is I'm sorry we're getting the in cross
actually from them Only thing what we're doing
is we're putting our data Now keep this
in mind Let us say there are certain
readily available convolution networks I'll drive down the
lane which all out there but say there
is one convolution network which is readily available
and say you have stored the weights off
This functions you in NH F at five
file or actually of fine or never filed
legal were because all of these weights I'm
not saying the entire network I'm just saying
the way it's over it wait in dozens
work to the size of the fatal and
what is all whatever we saw in the
above examples which we kind off Not if
we're not able to clarify rates are there
Now what do I do I asked what
I request this particular model toe import the
weights to my model So let's say this
is your morning You have created an executive
click off this morning Now what you do
is directly pick up the weights and properties
You're interested Okay Once you drop it in
the infrastructure what is remaining now So you
have just got convolution beats What do you
have to do now You have to bring
your F CNN here that's fully connected Knew
little This is We can easily do it
Not a big deal So this is what
we do in the industry site We do
not write much off the CNN's And even
if we have successfully able to put a
scene in a formal training and correcting it
will take a lot of very so I
will say majority off your implementations on the
industry point of view you will be using
transport Not what in ah in this certification
at least for for not tour I think
three weeks we will be handwriting some of
the scenes And after that we'll start picking
up CNN's off choice as this Okay And
uh that example that we saw we will
see in the court Let's see how we
can solve it off Okay any questions aren't
answered Money The concept part of it I
would show you how we do it but
this is what overall goes around erratically Take
the rates and use OK Any questions No
Perfect No All right So now let's try
to uh he was a people eater So
today I really was one p pretty You
mentioned Lord of Networks Now for first point
it will be very confusing immediately so don't
expect before I don't expect you guys to
master this networks in one morning What We're
going to Louis I will pick up each
network every week So let s say interests
session I will become VG which is the
most important Probably next session I could show
something one lx something on alienate Or I
can give you something Toe one really And
pulls that we can go to the complex
networks like rest nets will imagine it And
although sticks okay and this is not it
there are so many available Okay so yeah
So for today's agenda you will start with
CNN architectures But we just took an interruption
off the CNN CPI was a deep UV
notice I'm not going much detail And the
most important transferred coming toe the architecture of
CNN Try to push our formula here Whatever
we saw right So Harish I think this
will solve our problem Except I will say
it will not to solve the problem off
How many What is the math behind selecting
the number of fritters I'm still working on
those days and give me a week's time
I tried to find out there are some
logic behind this toe Yes Yeah Okay Even
that in those sorts for that Yes And
also I will request over Great I think
the professor who's teaching this if he can
share some papers with us So frank so
far I'm not able to find any particular
good restaurants that I can say that this
is the matte behind selecting these Maybe what
I feel is as you said they have
also decided first what is the final final
can fully connected layer on Then they decided
on this number serial engineer innately maybe I
also think but much because it would be
some logic It wasn't And normally I cannot
put anything Yeah I feel it is a
little weird Also I see if if you
see or does it means we have 20
across 28 6 filters So what if I
might reply 28 into 28 So let's say
I have 30 dough into 32 now when
you modified relating to 28 then added five
or six number of times I'm not sure
we're gonna get the same number back to
be very frank So what is happening is
Asper aware analysis or export our knowledge and
see And what is happening is we are
converting See if the strike is one then
definitely you are going to have the network
with Slope likely So one particularly may just
getting converted into many features That's what the
six insurers So I used to take it
as feature maps I have to say they're
six feature maps of anybody That means I
can pick anyone off them put it to
one off my convolution network So he fully
connected networks and try to find some information
out of this if possible Okay that's what
I used to date But now I feel
that is not enough I need also camo
produce apart from this Internet station let's ignore
this Apart from that you can not likely
see 25 minus Fife That is 27 We
have ST stride as one so derided by
one fighting is done zero plus one So
if you look at it 20 is the
one It was still doing 28 minus I
do divided by do plus one You have
another 8 14 Yes Woman of words Pretty
good on this Part of at least 50%
off the stuff is sold Except now if
you ask me again how they came of
a distant work now So I will say
it is a Lord off mix and match
trials happening So it's not a one short
job They're okay They decided this and they
got the best actions Because if you look
at it they are currently working on 60,000
barrels Okay now what are these networks and
how we can use it So if you
look at the highlighted area that I'm just
drawing this is what I am interested in
So if I have already made network open
source network for all of us which can
identify hand return digits So your new relatable
project if you remember 765 I know they
were not handed in but anyway we can
correlate us if I can do this What
I can do is rather than writing cane
and rather than writing that mural what I
will do I will ask this alienated rates
to be dropped onto my network and then
designed my own fully connected neurological depending on
how many number of outputs I have and
digging for Okay So these other beaches now
if you if you expect to use the
readings onto a face recognition system then it
will not work to be very sure Why
Because please remember this is what is important
So I am a specifying is tomorrow and
you're picking up this You should know what
it will solve Okay All right So this
is it This only vote for the 100
it's nothing else We're gonna play Yeah if
you're importing the reeds Okay If you are
replicating this network and giving face as an
input and trying to get an output and
just check cowardice if you get a good
output than you can say that yes it
works for space originals Got it But for
importing rates it works only 400 because they're
trained only on that data Okay Please say
if you're using it to estimate then we
can apply it only for the handwritten digits
But if you're trying to replicate the same
thing same architecture then we can provide the
face as whatever inputs we re weekend Yeah
So when I delivered to this court to
you guys not in today s court we
are going to use video You're going to
import of it easy So what do you
guys can do Is you Can you have
the network in front Off you You pick
up your older data that amnesty s creation
data from your previous project Pick it up
Try to replicate this network and see what
you're getting is a simple Okay So how
do you replicate You use the same values
And at the end when you convert connecting
this fully connected near Latakia multiplied Easter That's
buring A simplistic models If you don't want
to multiply all three of them five into
fight 25 and you give 25 as your
batch cells Okay so at one short 25
will be picked up productive And what is
if you want all of them All the
filters to be put in that one short
You can do that We're always you can
become one by one Yeah Surprise yourself It's
good if you play around with particular because
it's a very basic one So if you're
good at this then you'll be good at
the next two nights So if you look
at the features it is 400 turn It
was published in 1998 so I would say
it is an outdated and boredom But I
have used this into my people pass where
I would go in devising projects It works
perfectly good No problem Yeah 60,000 parameters If
you are looking at your current collapse then
I will say it is not a very
big network If we're looking at Jupiter yes
I would say it is heavy on Jupiter
The dimensions of the image decreases as the
number off channels So if you observe the
dimension is decreasing But number of chances in
please artoms I will say other than chance
What happens is when you say channel we
get confused with grayscale analogy Another case I
would say colored as feature maps So each
square that you see five profiles square carries
some kind of feature That's what it means
The activation function use in the paper was
sick more than manage But now what we
do is you don't have to use our
basic warning that unless you can use that
you know Can you please both of them
Merrill No problem if you want Good All
right So this was your uh are you
seeing this network Okay moving on Okay so
some pointers here is a tip anyway Neurological
classified It works properly on 0 to 9
Data 100 digits Children do various transformations like
rotations and scales So if you do a
recommendation also it's gonna work whatsoever s reaction
data will work for illegal armed with this
new Baraka was used by banks or recognize
100 numbers and digitize checks So still on
a very basic level Yes they do it
So what they do is if there is
a check consider it done If that I
started numbers were done on the check about
what is a payment under the city's signature
over there Anderson In those cases if you
want to identify the digits or define and
identify the check number of the dead you
can easily put the check below the scanner
and pick up the daytime process Very simple
You don't need a very heavy heart that
for this one So and it's a four
Wait Slaves if you observe here 12344 weeks
Okay No If you guys are undisturbed this
one quick question for all of you Can
somebody tell me if I put on Adama's
in my optimizer Let us a d a
m Adam is going to work on this
on this We sometimes think about how my
back proper is going to work Is it
gonna open Wonderfully connected on it would work
on everything I think For evidence I think
And what is true I do Okay In
the first case if you are using the
whole network as Attar's yes back prop is
going to work for both off Then I'll
show you how back problems for this You
people know how back part books Okay No
leading toe This Another question What if I
import the wage to my natural and I
use my own fully collected neural net and
go for that In that case back problems
going to work on both off them No
no You want my question Let me repeat
myself What if you have You have Ah
you have important So this is my import
off leads from the alley net website to
my need wolf And after that I'm building
my own fully connected neural network here something
like this And now if they put Adam
Adam is going to work on this on
our team is also going to work on
this I think it will work for me
for that part which we have customized Not
for the important part I feel so not
sure No no Perfect Right Because if I
back propagate and if I change my rates
there is no fun of importing That's by
every important debate because the rest pondered wits
already learned rates So if I back propagate
here it's not gonna work So we have
to make sure that every door we freeze
these pains whatever we have before this were
freezing they are not getting And this side
will be a sighting in third or fourth
Sure Yeah but there's a very gender car
question So when you go over and nwr
when you look at some of the most
important deep learning revolution or CV questions this
is what they asked Uh so this is
one concept keeping this in mind Let's try
to do complex or let's try to make
one more mortal Out of this which will
be your electric selects net is an extension
off Alina Begin say And why do we
need this is Let us say the earlier
one was able to identify the hero tonight
What if I only one even I want
to identify some of the objects or some
of the faces are any other things which
is not even number which is more than
a number If I want to do that
I need bigger We are architectures What Their
balance They're simply increased the layer soft convolution
and pulling generally fell into That's it Okay
there is no other difference And if you
observe even their f CNN will differ because
off number off changes So the goal for
the model was the Imagine A challenge which
classified images into 1000 classes for example has
60 million Balham Inter's compared to 60,000 vitamins
or so you in this increased Okay So
when this increase from 60 cato this one
even the capacity to classify images increased for
us now it could be anything if it
comes under those 101,000 classes You can use
the weights really made from this network to
your nickel It uses a new activation function
deformed And this paper waas convenes the computer
vision researchers there Deep learning so important from
this is blah blah blah And this is
how they have done this If you want
to see the devil a structure they have
taken 1234 44 heavy layers But if you
look here when something is wrong in my
diagram if you look here there are more
convolutions Give me one minute We have one
convolutions we have Uh yeah we have a
condition here Evolution here This is no convolution
no convolution There is a convolution here and
there's a revolution Yes I have 123456 convolutions
and do similar lists There's no change over
here All right so this is there What
is your Flexner It's an extension Please remember
this particular number is very heavy Let's go
to the extension off Okay What made Alexe
next successful So I will say architect er
it won't believe that because anything you mix
and match definitely a vomit Something order for
Ah we can say overlapping max bullets So
if you observe here what is overlapping Max
Pulling its If you observe we are doing
in middle We are doing for one Do
in front of the one you're doing for
Okay this is Arctic minutes You don't have
to do always Max pulling after confusion Ah
a new function was university use Just a
simple on dropouts cropping the documentation and inference
off conditions influence Our commendation I would say
is um let let let us today take
the case Really angry with this article What
that more or the less If you look
at the important part this is what it
Saul's Um this is okay It's not a
big deal or no deal Yeah this is
what do you need to remember about Max
Wedding You don't have a lot of it
is And I think architecture from these are
some important successfactors ex coming on Too easy
No increase Still more number off pulling and
convolution list You'll end up with this particular
little on Let's confirm what all of them
It's easier to 27 to 27 3 on
this was dying to 61 and here we
have could only afford We're going for three
Onda fully connected This falls in the night
Six folks So if you are left again
for your doors you increase the number off
convolution layers Or if I elect snapped to
make something Luigi 16 is not the latest
version Now now we're having 90 But both
of them were equally so for me if
I had been some implementation If I'm using
video to 16 use me good accuracy I
don't know tonight So I didn't really heavy
identity Yeah Other other remains The same thing
ever expect no change after this So what
are these I'll go What are these networks
is there were some challenges in which some
off some of the people came together And
if you ask me how it's about mixing
actually really for yeah Uh it is no
use Some of the important things is from
60 million Now we're jumping to 138 million
barrel Mater's Yes it holds a total memory
of 96 can be poor image for only
forward propagation Most of the memory are in
earlier Less so If you just observed that
you have two things one is your network
and one is a ref CNN So we
can say that most of the memory kind
off it goes over here This is the
heavy partners in like that number often does
increase from 64 to 1 28 56 to
physics to fire undo and fire and do
has been made twice So they are just
replicating some number from the necklace You don't
have to remember on this Whenever you want
to use it just come back to this
particular thing and try to replicate the network
You have the numbers ready You just have
reported in Cara's AG then scare us that
for that particular command you use I leave
Anybody is interested in paper Is the paper
cashed I would say it is heavy So
first get comfortable with all this and then
go on the paper Okay On as mentioned
Over here there are two words and 16
and 90 but more of the lesser does
the same job whichever you want to use
it Good Now comes a little problem Any
part for now These were the three simple
networks which we can visualize Now comes a
little different network We call it our Yes
Nick rested Now my question Dual A few
ISS What if I want to use this
particular hardware Whatever we have does OK It's
a very good night dog Agreed But now
I want to use on a hand held
device and with infraction or second if I
given imagine we didn't fraction of a second
should give me some out It should put
a square around my face and say that
Yes we are different is you Will your
device be able toward us Okay You mean
computer Yeah So if you have a mobile
phone so William Mobile be able to hold
us up This this off this heavy from
closing Maybe As you said though there is
another architecture called mobile How did that to
you Yeah mobile letters An alternative version of
idiots But let's not go there Let's tryto
think What if what we can do here
or uh okay Let me My example is
not corrected Let me try to uh create
another example Guys Let's take one more example
Let's not think about competition So let's see
I am currently having one image which needs
to be given as an input and immediately
within some time limited t I'm supposed to
get the classifications or court along with some
tagging or some some classified Now as you
saw the number of layers on more over
here so we heard one layer another layer
Thirdly fourthly like that Because there's so many
layers out there and it is going in
a sequence of man of sofa We have
not broken any other sequin shin already say
float has off such correct Now what if
I want to increase the speed off this
name before Cherie to ask for You know
the saying Take five inputs from your base
the current dynasty Okay I want o make
a time period saying that he knew now
being you should be far less than it
means It should be faster What should they
do Are you talking about emerging software like
uh Gordon your media debts and then No
no no Let's on go there Let's say
if I'd make some changes in my current
network I should get some good speed I
wanted that Disney talk is very good Perfect
We got back I don't want to lose
it What if I am not happy with
the thing box Maybe Teoh increase the hidden
layers I mean it might reduce the pain
with less then the family easily Yeah but
simply fight Ah input in our so exactly
So how do we do that You are
perfectly right How to simplify this now This
tribute Okay Okay So you got your talking
about parallel processing something like that Yes OK
find that could be a possibility for like
me right now What if I have a
parallel process What I am having a Dubin
moment where I'm going by processing Okay friend
This could be one possibility but we are
not changing the architecture right What we're doing
is we're distributing the data I would split
my daytime So many and then I will
issue Could parlay until my people It's OK
What can I do in my network Okay
Let me give an example I think then
you guys will be ableto pull up something
Today's your to see when this is my
computer screen that you are able to see
okay on DA for example in front of
my screen I assure you half off the
image like this Okay now as soon as
you see So just imagine that you are
not able to see this particular part of
So as soon as you see this thing
in my screen What He was human Toby
What shape you assume it to be Everybody
would say Yeah it's something okay Or is
if I just in the time frame if
I'm just grueling one image on my screen
and you are able to see only half
part off it But yet you are able
dough Describe it right Water this You will
have some seem as I'm sure in your
mind that yes he's trying to show a
kind of square And as soon as it
comes in the picture in my friend everybody
knows the ites A square keeping his analog
in your your concept What if I can
give some early in sake that I can
take up the output off this and Floyd
through this So definitely it's going to go
here So let us say output from here
it goes here It takes some time frame
Meanwhile early I throw the output feels So
what's gonna happen This particular network is going
to make some are foods and start doing
it in future So tell our images getting
processed here our output will reach deal images
So there are certain cases where if you
look at certain parts off your image let
us say in this particular part this I'm
study one of the feature map Looks like
this And as soon as I give this
leech Alma let us say the future Michael's
here as soon as I give it to
the next to next network and if it
gives next to next to this network we
could be able to identify that This looks
like a cat Possibly it's a cat So
until I get all of my future maps
all of the sinking and all I would
have easily I didn't different this able to
get it because this is something similar to
what you say But here we're not splitting
the data Parallel T I am forwarding the
data and avoiding one off them and forwarding
it further You will get it everyone amid
Dhruv Jakti Car Suresh Corsicana So yes cause
logical Yeah yeah OK good So this is
what rest net does now The only challenge
so Nobody challenged me here saying that OK
fine If I do that not to say
as I said that is one convolution layer
Max Willingly Convolution Maximilian condition There are so
many like this Now what I do is
by default There is a sequential order Yes
it will follow but it still it goes
to a fully connected But if I taken
are put from one north and give it
to set this note don't you think the
filter is really a mismatch I agree So
let's say this is 28 across 28 Cross
three for example about And when we reach
here it could be 16 cross 16 hours
six fights Do anything This is gonna mismatch
is on It was in convolution Every step
We're reducing the size read without put off
This particular one is going to miss match
with the next door next to next one
So what we do to solve this We
keep the same stuff for all of them
It means we're designing it Work which has
seen for designers See him Actuacion All right
This is how it looks like It looks
pathetic If you located it makes no sense
at all Such but yeah try to understand
that we are giving an early opportunity skipping
one convolution and we're going for them All
right So it's a very very deep network
as register ana because of vanishing and exploding
Grady in problems now Or do you mean
by vanishing and explode ingredient So I will
I will True down the line So let
us see I ask you days you have
done the model you have All of you
have cleared the model on statistics Yes The
first more Do yes Can somebody name that
three distribution we have done in the statistical
More did And somebody named this a normal
distribution OK boys in distribution OK on is
a binomial distribution off Manimal distribution years Okay
And one more Okay let me I end
Estimated your ways Yeah Now now Five It
reason guys started stews Oh Okay Good Drove
it Was everything okay Good Okay What if
I say what were the statistical tests You
people What about statistical destiny North So there
be test You guessed I'm Noah Noah effect
their desk Their national study would less affected
Okay fine Okay then you're gay That's ruining
my example Okay See what I was trying
to show you Days Waas Okay good You
neither Magistrate We don't know It helped me
to explain my stuff See what happens His
name when you have ah particular data which
is passing through a month people neural networks
might people neural networks you know No let
us say the data is entered here it
went through Lord off these convolution on pollution
There's whatever pulling less whatever Let's finally it
came here now say you found a loss
here Do l by the off would want
So let us said this is weights So
what you do immediately you go here and
change the Ritz It's still in the next
propagation If you're not happy we immediately go
back and change occurs At least I can
say that at least till here If I
change my rates I'll be able to at
least control So what I do I need
to say is the rates which are there
in the earlier part of the network If
you have a very deep network like this
this particular weeds are going to vanish more
or the less they will make not much
significance to the network To be very frank
this is called the problem Off Vanishing Radiant
We're going to revisit this concept in NLP
next morning under an NPP having a tropical
island and treacle in your little So there
is a problem Same problem Brennan is a
deep network where we will face the same
issue And to solve this we use on
a local last year long short term memory
So well how some men Many where we
are going to store these weights down the
line Okay if you look at our rest
net moral also this is a very deep
network And whenever you find the world deep
network always say that or always remember vanishing
exporting aliens That would be a big issue
vanishing Gillian for these people explored ingredient For
these the ingredients are going to a pretty
high because Lord off changes will happen which
is very near without putting So this is
what is your listening next Uh that's on
You mean one of the less Yeah So
this is orders Important points So the architectural
off arrested 30 for now You have many
worsens off This This is arrested 34 that
is resting at 1 50 has invested 50
years The concept has changed Only thing is
the number off layers that you are going
to see here with British the sleepless Okay
all all our tree Crossley convolutions and same
convolutions No change So we keep uniformity Ah
keep it simple and design off the clock
because we don't want to play around because
I want my out put to work on
any of these convolutions I keep it as
simple as possible Okay on if you observed
that I know fully connected lists and I
think that said that's all on One thing
to remember is the dotted lines in this
case when the damage Okay one weathers my
daughter main Okay so there there there are
two things I'm not sure if you are
able to see the dotted lines If your
input and output are kind off racing each
other that means you can switch or some
of these lines that these are optional connections
that the abuse it's not necessary to take
each one of them interest So if you
are designing it that you are surpassing only
one your little you can have some of
them as dropouts saying that we don't need
them So these other daughter So we will
see I will implement one of these restaurants
It's not in our course It's not in
our standard for Macchia but I don't believe
this I would implement this rest in it
and I will show you once you're comfortable
with the 1st 3 Algarve Networks Victory Lewis
Let's see how we can design status So
first thing is well designed Second thing is
will directly important bits and use them Perfect
So this is orders the expanded our zoom
diversion off arrested 34 Yeah 34 presents a
total number of deep How deep the networks
No coming to how to decide how many
layers to bacon on the spot if it's
so very simple If you have around five
years in a dog it's a very easy
network Okay so it's not a leaving a
drop it all If you have a known
painless including initialize er shelling batch normalization isn't
good I can see you are still an
easy one but a little complex Little leave
us comfort to fight If you go to
30 and 100 these are the real evening
looks especially answer that this one is very
leave And in this network if you are
using this your guide and if I have
to skip the connections because the time is
going to be very long So if we're
planning to have 100 layers are you in
for delays Please make sure that you have
skipping the connection The one which you didn't
dressed so more of The less I can
see these are examples off the rest nerds
LSD ends on all this fun on anything
about 1000 years I'm not sure I have
never seen in my life or we have
no idea how deep is going to be
So if you want Oh look at one
of the layers which is very deep This
could be an example of the inception It
from Mobile Hey look at this And they
have a lot of connection Lot of skipping
is happening here So what is it The
inception Adore consists off uncoordinated blocks off inception
models The name inception was taken from the
mainly made You need us sometime A Maxwell
Bloch is used even before the inception morning
to reduce that dimension off So please remember
it is not always necessary to take the
image as it is the way we used
to do BC a same thing began word
heroes now ever shown you any times how
to reduce the dimension in image processing No
no Have I shown you anything on our
toe in quarters Anything No night No right
shortly So if you remember is true It's
really PC An essay and a TSA I'm
not sure you when you have done that
Um have you heard of 40 years Any
days I'm sure you one more dimensional election
technique in machine learning today But apart from
that if you want to do same thing
in your little how should we do it
The first day was nothing What You're convolution
factor solution was something like um sinking the
day tights But if if you look at
convolution as the direction there is a problem
let's see how notice it is image you
are having here which is the same 64
across 64 and after convolution before flattening you
got any made savages Five Cross life Arguelles
I would not say image I got some
data which is five cross faith What we
have done The way we are able to
shrink this up Okay Now can I use
this for future processing If I give it
to one off our networks Saline a Alexa
Donald will Alexe alienate be able to identify
that this is apart off a lot of
that image like this Yes I know Can
I do something like this which I call
the dimensional reduction Is it possible Think about
yes we can do that We can do
that Okay we got one Yes Anybody else
thinks it's not a good idea is a
good idea Okay It's possible This Okay fine
From my side You guys can try this
out if you If you can do it
If you put a CNN at the end
Yes it is Possibility it will become your
CNN again if you put enough CNN here
But what if I want to send this
to some other networks Other networks will not
have the recordings yet You remember We have
done some some including here You have used
some freighters toe in court This data every
given them the d quarterback It is like
that cipher I'm not sure if you heard
about this Let us say you want to
secretly communicate something What you do is you
implement your cipher There will be a filter
with you Multiply with all the things and
could create some new numbers Look like junk
data Now this Seifert record this you have
to give Divide the whole later by the
cipher again Then you are going to get
it back Take No this is what I
call it is a cipher Whatever we have
done here are we providing safer along with
that So tomorrow if you want to recreate
this image use the safe We're not doing
it right So what if I want to
reduce the size of the inmates and if
I want to recreate it we should be
even able to replicate it back OK But
in this case what I will lose if
this was 64 close 64 I cannot really
replicate the whole thing back Cannot have the
whole sample England So what I will do
it Yes If I want to reduce the
size to 50% I was eternally do custody
But still I will be let us say
if the number seven is here I will
be able to Wrigley Reapers and seven back
This is called orto in goodness I'll show
you pretty one implementation also but a very
high level This is how it looks like
And if you ask me what is present
inside these ah networks there are two things
which is present one It's called encoding Okay
in court And when is equalled Anybody here
from electron ICS and communication background or communication
background or at least special DST part of
anybody they should Theo cameras on Yeah anybody
something's background even from computer science background You
guys would have done it Uh and Korda's
recorders No cryptology or something I have Yeah
you can court starting Correct You could correlate
this concepts They're saying that we will have
something done on an image will shrink it
and then later on will pass it We
will again recreated and pass it to our
Now Why do we do that There is
importing have reduced my dimension Everything today All
right now what that is in court and
the court Very simple It could be your
fully connected nearly designing network whose outputs are
little lesser as compared to you originally How
many were layers you wanted How would you
design these layers depending on this So fast
it for you to decide that According Seiss
how much you want to record it Okay
And then finally you design in worse You're
a little Keep training them unless and until
we are satisfied that good off them are
almost This is quite auto including It's not
a very popular way to do it because
definitely in future If I have seven this
image will not be one makeup fix Okay
I wonder Take HD images and shrink it
And if I take another the image drink
like this Definitely I'm gonna lose some date
out off Everybody agrees to that We cannot
completely recreate this no matter how good our
network is here Okay So this is your
new network Simple one does also in your
you trained awaits back and forth Still they
want single All right so this is an
order in court I'll show you one implementation
down the line Yeah So what I will
ask you here Yeah So Ah yes So
in other cases where Google has done is
they have done Max pulling It's also you
can think about Max pulling as I mentioned
Introduction Yeah So let's say if you have
an image say my favorite number 30 Do
go study Okay Now if I want to
create the if I wondered replicate this Imagine
to 16 Close 16 What I'm gonna do
I'm gonna Max believe I do 32 by
doing 16 Very soon I can do this
Are you going to the one which I
sure do Anything is OK So this is
your Google next harm You will take this
particular network at the end off the model
so that you're very comfortable in your own
get countries Because by looking at itself it's
pathetic It's horrible And to understand this I'm
not sure you and it is open source
I'm still seeing door difficult bound ordered else
what I lose We'll do a very good
in that theater to get you into this
Where does it didn't have the guard Hold
on to this How can What is the
application off Google it All right Now if
you compare the most popular really made a
lot on networks available you will find them
over here If you look at this the
most stable one is reduced down the line
I think almost all of my implementations you
will find religion So it's very simple to
work immediately Okay And look at the accuracy
Also rigidity is not very great It covers
around 65 75 But yes From the paramedics
point off you it is showing one of
the highest parameters More the parameters better will
be alone Alexe Net Onda We will lie
somewhere here Restaurant is on the here Ah
yes Not 1 50 Duel Okay so 1
50 doing this engine having more parameters That
more number of the places compared to 18
34 in 1 50 a distant 50 a
distant one dealer ones or different versions that
Okay so this is how you can visualize
this party So I will I would say
if you guys have decided to transition your
career on the computer vision then you need
to know some of the most popular ones
like these I haven't elects Nick May as
well and said it's Nick 1 50 If
you know these three algorithms you can say
yes I know competition Yeah because they are
the top of Hamas It does he accept
election It Alexa is the one very basic
one but needs to work enough Papa pharmacy
and also try to correlate them with mammography
Just good All right Coming toe Concept of
transfer Done It's too much of theory will
come down to this Very simple Bigger I
have got a source fast Let the system
tasks Generally I am putting and I'm running
lot of harder under this and and putting
it to my knowledge Now what do I
do is I want my target Tusk Yeah
let's have a real time application but I
want to use it I get my later
I import your learnings on my loading system
Okay Night Classy family stuff But you don't
have no more You go the classical traditional
machine learning in your mortal and then use
you more No need to do that Use
ready made learnings to one So this is
your transformed money And if you look at
the learning curve balls to be very frank
this is a better obviously and majority of
my implement isn't that I do I already
made I Diggory Magneto rockets I don't take
a pain are doing CNN's Ah now you
may have you can find And why are
we learning You will understand CNN's they use
offered after week number four where the enter
our CNN fast RCN and your Lowe s
is the older complex algorithms that the internal
at the time again will we have to
use CNN's We ever use somebody for the
concept of building CNN Okay great things were
done to oneself Theory What do we have
today is we have got a very simple
ah data center where we have got the
hospital already into Destin planes Okay And what
do we have inside What off them We
have same five folders and each folder is
having some flowers which belonged to each one
of them Okay so this world is a
data Our job here is to classify the
flowers from the testing part of it So
we train it He was in order in
India and we'll use this particular chunk toe
classified as him But it's bad So the
upper ones is the upper quarters for the
people who want to use Collab So I'll
start from here Ah so this is my
get us So now I think it was
a comfortable on the sequential part Convolution duty
You As soon as your second relation duty
Max pulling comes into it flattening it dense
and drop Now I'm not sure with version
2.0 that this is gonna work Good writing
from from here This It looks like something
like this from spencer floor dot Get us
And then everything goes OK and that makes
them more to give You might get some
evidence If you have a decision I'm going
on directly to the classify So if you
look at this I have got 32 three
cross three Uh what do you see Freed
us and I were in good shape for
64 Prague 64 Cross street So very simple
Padding is zero Stride is one Just put
the Formula 64 minus three That is 61
61 by um striding Ah strong strides One
as usual Plus one That means the answer
is going to be Ah 62 No no
no no no no One minute answer is
going to be your sex It'll 60 to
1 You know Then why my it's not
looking was the next one Rex honest three
Cross Street 22 Okay So rather than taking
this approach what I would say is we'll
take this particular approach Say 6464 is what
4096 Okay 4096 When you pass it through
your max pulling function off to cross through
What are we doing Basically we're dividing it
by Duke So when you do a 4096
by two you are going to get 1096
When 296 again You Max Bernard you are
going to get I'm sorry No 1096 or
you're going to get to zero How much
somebody handed me I had to get my
phone Here are 9 60 ordered by who
is 20 for perfect No news You know
forage when you're going to read it Max
pulled by two on the right is by
Joe You are going to get ones they
don't do for right And one digital way
went Now if you look at my a
lot of nice if you look at our
input for ah fully continually Look our neural
network ended if I went to West Flat
Okay so take this approach How Solving this
problem Yeah So as for me this I'm
not changing the filters side It's working Good
Now this is not an optimal solution We
create some problems straightening Give it to you
Waste around with the layers more and more
but make sure that at the end it
should match with your input off your for
Okay If you look at it what we
have on the first layer off we have
32 3 Cross tree is my import standard
stuff I'm using more changes here Sick and
minerals A standard 3rd 1 standard Same thing
This is five And once your entire fully
connected new electric I'm saying give me some
drop out our Dan Slater could be Is
it the right by four of this for
example and activation function Israel You And finally
if you observe I have what plan toe
34 and 55 classes So obviously my heart
Fortis fight on my layers start to nightingale
border This is a simple CNN that I
resign I'm moving on I wanted to show
you something a little bit more here wherein
we are calling over Adam Okay And we're
putting into our compiler But apart from more
learning rate if you remember we have not
used these functions so far Now what are
these How are you Check this So one
way to go and check all of activation
Thing is you can goto callus documentation on
and go toe optimizers He will get list
off all optimizers available which is STD animists
Prop 88 glare Grad am is great Idiot
is uh Adam Adam is the most common
one and his GED is also most for
movement And if you have a deep network
you can use an animist propagation Alam s
broke Yeah I'll show down the line how
you can use this one Now what are
these x started So these all hyper parameters
are defined there anyway So the first thing
is you're deaky factness No and everybody gets
What is a wiki What became talking Did
you remember Ah Grady in dissent that we
have You know it's kind of begins down
So the standard values if you don't even
initialize them their standard values are going to
beat this only So I'm not playing around
with this anymore I'm just trying to show
you there tomorrow If you increase your learning
rate you're DK is going to be more
steeper So if you look at it be
talked to and Dick Eric if you add
them up we're going to get standard Answer
is one So please keep a good check
on both of them if you are playing
around but more with learning Lee And also
if you observe there is something will be
talent and Peter what off them re present
Declare it so this could be the degraded
for the first estimate First estimate letters say
it could be um the first door function
that you from your backdrop And this could
be estimate the one from the second estimate
The 2nd 1 Yeah Usually we don't play
around with this even on web you will
not find many implementations were friends for Just
wanted to give you some uh hyper parameters
here And the last one is absalon it
Make sure that we don't miss classify anything
all we don't basically go into infinite distance
So in none off our implementations were doing
any operations with zeroes If you observe that
you're not adding or multiplying or dividing And
some of the neural networks are machine gun
and part of it Carol as we do
some manipulation zeros in this case it will
make sure that we don't do it So
by default we keep off Julia not worried
about this So these are some of the
hyper parameters And as usual we're using cat
medical cross interview because I have got if
I outputs and I made fixes something What
Thank you Next thing we have not done
any data manipulation that cleaned our data So
now what I'm doing us I'm documenting my
data Now if you don't want to write
a separate function for argumentation and if you
want to do it As for stuff what
you do is you import image data generator
with the function So you give a lot
of parameters Some of the important ones that
she's wearing Ri scaling the image you are
having rotating the images Zoom in the image
flocked flipping holds on to flay particle flip
all of us So this is the way
you can create some kind of augmented images
If you're not happy with size on later
same thing you can do it with your
destiny Downs Okay next thing you call your
test data from the directory and you say
flip it to the size one size by
64 64 because you are resigned to CNN
Rich in port 64 60 three He's in
the mood of chance to match it up
Next Total number off images you want to
be You want to pick up in one
around so depends on your computer If you
have high end ram for it I will
say increase the size of this Now when
somebody told me why do I need to
focus on increasing my bat size What difference
is going to me Even if I specify
that If I don't specify what is your
understanding of bats Ice What do you mean
my bat size How much state I process
one parent book Good So why should we
take 30 Do here I can take a
more number Can it So please remember if
you want to increase speed up total Amorosi
if you say 20 e books and e
t boxer you are picking up around you
Are you are your computer is keeping really
32 you made is to be processed What
if I give more flexibility to my competency
I will double it Just observed by changing
the size of batch just observe your e
books The total time taken by 20 bucks
to run with 32 the total time taken
messages before and let me know where did
yourself It might slow down Kind of Yes
Okay So again it is very important Basically
to selector So what I usually do is
I can be like this number Why do
I know logically it's not correct I do
It is by doing keep some family number
That's a pick So there is no logic
on that But yes high income through this
can't work My comfortable crash predict 64 when
we need here Now moving on Same thing
boy Our best I'm taking the same part
over on Finally you are fitting your data
using the generator one so that you can
get good augmented images and say this is
my training set This is my validation of
celebration data and in your e box say
I'm under run 20 box You are seeing
something new here today with this called steps
But the books now What do you mean
by that In one e book I want
that particular e book to run these many
number of times So what is 38 to
3 382 threes A total number rose training
images We have divided by how many of
them are picked up by the time 30
do from there from here So I am
saying is even every day in order in
De Gier cannot return in teacher and each
e book inside 80 book You give me
the number of steps so either you can
have more number of heat box All you
can have internal steps with me Before they
were suit I gave you about adoptions So
far you would have seen only this late
Okay so this is what you can do
it I say this is a better option
so that we get to know that if
in gave money park were running herd and
19 times again and again probably I'll say
we didn't fortify Balls will be able to
get better actresses if you observe here the
amount of accuracy amount of accuracy changes pretty
high So this thing is turning pretty good
rather than having 40 50 100 books were
Wait a longer time Even this will be
longer because you're inside each pocket Having is
many steps so definitely is gonna take longer
But the accuracy part of Britain be pretty
good So if you score down the line
I ended up a 20 box after certain
eat box You observe It is not going
for the accuracy is going down Enough going
down There could be multiple reasons for that
The first reason could be the wavy off
chosen their steps but they don't even do
it Second could be the F than too
much of a commendation My So that we're
not able to find some partner 3rd 1
could be our learning rate and optimizer And
the most important reason could be the CNN
live that we have made Okay so I
eat yours totally to make four less Why
Because of what alienate had that option So
I did But what if I don't want
this I want to increase my accuracy Yes
I give it to you guys My next
Friday or Saturday Maximum send me are inputs
how you get improved This all right You
don't need to do this on the industry
side because we're what Ready made networks But
anyway if you play around with these numbers
and also check the block that drew as
it will help us to formulated the lead
one layer add a layer or I have
taken a maximum size of to cross You
can take four cross for ALS No problem
Free cross three also know And I have
taken standard for dust year You play around
defenders and see how it happens But the
only issue is don't run 20 box You
are going to waste your time looking at
your monitor So try to run less number
of the box and see how much accuracy
is increasing No not really Okay so here
we award around 80 losses 49 Onda Validation
losses 90 and Malaysian accuracy 64 So I
still say it's not a very good morals
was a tenant Perfect Okay good So this
is one implementation one seeinit No What if
we Oh yeah So now a little differently
According this so giving you guys hand off
how you can see if your morning So
first of all what you guys do is
every time you kill your corner you come
back You're reading the moral If you don't
want to do it use this two options
there in you have an option to save
your weights Whatever awaits you have foot but
you can imported in the next network whenever
you needed are you can save your home
network hazardous So this is one way to
do it Another ways to do correlate are
what is called job There is a library
quite job which doesn't Or there is something
called pickling If you want to see all
this state today I'm sure down the line
if you are happy with this usually in
CVS comfort divisions we do this We don't
know job living for machine learning against any
struggle in machine learning We don't have weights
to the store but here we have an
option Either restore the classified itself Arthur any
of this So now what I've done is
that because that's what and I don't have
to read on every time I open down
the line What I'm doing I'm calling back
my model calling but my rates would have
kept together Call it now Once I do
that what am I doing is I'm reading
some random flower or set off lows and
uh next time changing the dimension basically and
reshaping them I'm Jane in the damage I'm
joining the values offered by normalizing here frighteningly
and saying Listen with the name of the
flower that I'm gonna get up Okay that's
really the dimension of the image that I
want to pick it up And finally I
learned my classes I predict that particular image
on for this Yeah So if you look
at our put is the size off my
image this is the emitted after expansion Yeah
Next is the set of options that we
have out of this options If you look
at the soft makes output V of God
and somebody who don't if this is myself
makes output Which one off This is the
classified answer Sunny Daisy Exactly The one with
the highest probability will be done in this
case Were identified anyways displayed here but yes
please look at the stuff So there's not
needed by anyway we got down so directly
So now whatever imagery gear was days now
what if I want to run it across
the whole destined eight engine data matrix Very
simple I'm just getting all of my test
data from the but putting the hyper parameters
and running the more launder that when they
do that I get out Envision medics like
this And if you look at this these
are our flowers that we're talking about And
if you don't want to do that that
is precision recall effort in support for snowboarders
Precision We know very calling What is F
one F one is a balance between both
of Okay I can organise the average within
both And what is support Support will say
how many images off this class are present
in the whole leaders The frequency So it's
something like our target I know you used
to do dark value cards If you remember
to check the target Same thing in this
case good representation But if you observe the
precision recall they're not that great So the
one which classified gets classified the bestest this
one sunflower All right so this is a
very simple you have a question How do
we read that matrix that is showing about
Yes in this case house 67 off days
east having identified properly Now out off that
well off them which are actually daisies are
identified us the next one dandelion 13 of
them are identified as rose Three off them
a sunflower and fire off the mysterious Okay
so find out if it if you want
insides out off this you can find out
the flowers which are very close to each
other So in this case you can say
that uh Daisy and Rose we can see
our day zeros And uh then daily they
look almost similar Why Because a misclassification maximum
Others if we look at the last three
of them which is uh rules on flowering
tulips the misclassification is happening Too much circular
by mistake is getting classified majorly as you
live and Rose are getting confused Okay this
is indeed good And if you want to
look at the correct classify irs look at
their diagnose elements They are the correct volumes
or feel classifications All right Good So this
is one simple CNN example 30 vincey Let's
try and do So This is one way
you can do transfer learning they assure you
but that learning was not from any of
the normal Better net what It was running
Now let's do Let's try to see one
more data set will begin Do that I
have a question on last Yes I used
banning Seem we're not doing biting that That's
what we mean Any disease Oh good So
what I did was I kept it very
standard So I want us to musically do
all the mixed match possible and try to
create your own Yeah Ah we were loading
the model that we were restrained at the
upper part Yeah Does that mean the card
not is restarting when we're loading the more
delegates Yeah Good No See what do you
do with a lot of lives Okay so
now you you mean to say s that
when I switch off my colonel and the
Navy start will I be able to get
the same model back That's that's what you're
trying to ask me right Do I have
to go reimport entire program or it's just
from the Lord mortal part Just Lord more
That's it But if you are putting your
weight onto your new model wherever you're loading
we have to make sure that the weights
that we had over here that this infrastructure
that we created here should be same as
this If your new say new network should
look something like this you should have at
least these many layers put the raids Got
it So it is like a machine your
machine and model under four variables Now when
your model is ready when you're predicting it
if you're predicting on three variables what's gonna
happen it's going to crash Why Because it
was not one water variable is missing so
you should match this That's it But yes
you are perfectly right You can do it
on any off the other courts not only
on the score You can import it somewhere
Insoles alright leading you to make sure you
have to get the good correct part of
the file Or that's what I usually do
is I put it in a local directory
But I'm using the right now In this
example we thought there are images in the
folder in the case 30 or in the
project that we have We have a H
five file which I think is the conversion
off images in tow Some So I just
wanted to unnecessary Low Who Why here here
Because we're also saving your in h five
part But uh is it Ah that it
will get Ah And what do you say
The quarter Part h five file sometimes and
sometimes he measures And based on that we
have to take it into account Because the
way this was the status it was loaded
is something Ah in a different way compared
to how we look OK so I think
you were not You didn't lose your second
session authorities Okay so you're not detonate HDL
basically What is this HD a former D
A former is a zip file You can
see which will consist off Lord Off made
This is okay Now each meat tricks re
presents an image over here Okay And eat
image has a target variable attached to it
So in your previous project if you remember
the target column was work What number is
this And the the sample part of individual
stored as a I mean this is worded
HD a former I mean our case What
we're doing is when you're storing it is
an H five for marriage or any other
former What happens is it it tells me
or it gives me some kind of mapping
that this is salient number one And these
are the weights off number This is late
number two and these are the weights off
their number Whenever I import this get bite
on interprets this indirectly starts loading these weights
in our current interested Got it I'm it
If you want to see in detail given
sometime I will make one specific case Studies
on how toe what other different varieties in
which we can important map that if you
do that you can do that Arts we
have zip former We have tar format We
have got HD A format We're gonna actually
a 44 run We were inch 54 months
Um then we have got Jake bag and
being d former Also multiple areas We can
we down the line in one of the
case studies Even what we have done is
we have done embedded That means we're practically
important The imagined acted ourselves That's our informer
If you guys want to see one specific
case studies on all the imports we can
do that No problem Let me know Okay
Yes sure That bigoted Perfect Only so can
we mourn Um it is it Is it
Gardner You're clear about a one more I
did not ask any off You uh project
is submitted right by all of you I
think even the school result Anybody wants to
discuss the project solution to let me know
where you it Okay The previous project Let
me know I dumped the solution Anyway I'm
not sure where they discuss a solution with
you guys on Please remind me they made
forget sometimes No coming Okay we lose Auto
included Last coming to this particular day does
it We'll do one thing We don't have
enough time to go through the whole data
sets So we will We will revisit that
It's a huge basically huge history So for
now what I will destroy you Raise it
I've been sure Auto import um findings and
how to load it on the network Okay
So just ah think about it That there
is a data set that I have which
has different breeds of dogs Okay And they
have the photographs and impotence and signed off
these breeds the same way we had flowers
Now we about different breeds I think we
have around our 19 or 20 yard unique
breeds of a little Now what are we
doing is we're building our own convolution Urinate
took as usual my normal pattern of building
So I'm starting with some numbers Say 32
5 plus fight Okay And my input site
is exactly same as the size of my
images It is one green gate close winding
be across three All right Next am having
the filter size off 64 across 64 assorted
started number of 64 filters for cross for
each gun sights Yeah and I'm keeping the
standard functions It seems I've got 1234 layers
And after that I'm building my densely very
And the total number of unique outputs we
have is 1 20 AM Sorry I said
90 but it's sort of unique Believes that
one drink All right so this is this
is one example of a CNN that happened
when I run my steel and down the
line Ah let me show you So when
we run the CNN Huh Yeah So in
around my CNN say for example Ah for
number off some number of people except for
one e books and e t poker have
taken a step off 8177 That means I'm
running 817 70 box as such When I
do that look at my validation accuracy and
normal accuracy I'm getting 0.0 it that this
is you know 0.8% of accuracy and validation
is 0.1 That is 1.1 personal actress underfoot
Morley Definite Yeah why you regard this Because
off whatever stunt tweeted over here by creating
a weapon Seeinit All right now what if
we are stuck in the situation We don't
have time does clear on with multiple stuff
of years In that case what begin to
us we have got standard v d D
16 rates Only thing is there is a
file which we have distorted Either you can
download it Yeah from here you can give
the reference we hear or again put it
in my local directly and give a reference
like this Whatever you wish Yeah I'm saying
this is my new babies Modern This is
my new mortar That would be And these
are nothing but a video 16 rates And
here you defined your location off your H
five file from where you are getting them
If you observe the farmer off this file
and if you observe the former dollar off
aware this fight there exactly seen So basically
the format is used to store Next thing
what I get this I ah what do
you say Ah Rhenish like like put my
x and y again So basically I initialize
my target column and training Now in this
only one thing I wanted to show you
something Newest ecru diem Diem as a method
off generating this status body of yours What
is the current state of if you want
to Because sometimes in the box are and
you know we're not sure how much time
it will take Definitely We have a timer
here but yes If you want the whole
stack pick your m will help you to
do that on it Next thing is what
are we doing is we're getting all the
images one by one And once we get
the images we are passing the images through
our base model What is a base model
based model is a model that were defined
by building our region So I am I
am indirectly telling them I don't want book
Take this through my new little my F
season for my normal letter where I am
going forward and backward for now you pass
on my inputs through my new It's as
simple as that All right And once we
do that we're building over fully connected new
leg of the back and part of it
the final part of it And then I'm
compiling it And finally we're running Do you
get it How I froze the last Because
if you remember we had not supposed to
back propagate this So if I would have
put the pages here along with our modern
art freight what's gonna happen It's going to
do back propagation and change all off I
don't want to do that so first itself
I'm running my input through our weights and
deploying them So it is nothing Your input
is there This is your input And these
are your rates your directly multiplying it and
making it else one particular matrix Now you
use this as your extreme basically onto your
final model Got it And when you run
the model if you observe the last accuracy
V God is around 98.39 plus so it's
a better model Linda Whenever are we clear
Raise any envision Under this the freezing part
of hurt and non partisan freezing is 00
Marty playing or current input Quit rates Exactly
So what I'm doing is in hurry I'm
having my images or my ex training data
I'm saying it Whatever weights I have got
no my reply with the inputs and make
it a bigger matrix And now use this
as your training data with your F CNN
to do front and back propagation Now even
if back propagation happens do you think it
will change anything for training data Nothing It
would change everything when you're seeing it Was
it elongated that this is in Europe and
what rates were tell nothing but some kind
of meters All right It takes some time
so it take a week's time for us
to get normal tow this way of courting
Yeah So give sometime Try exploring this So
now I will deliver both the courts to
you What do people do is in this
particular earlier court that have shown you know
instead of using my model you used this
immediately 16 months and see if you are
able to in price Okay And this from
here down the line it's a universal quarter
to work for any No problem at all
Good Everyone clear Let me go through parish
A cache of meat drew for akashic Randy
recoups or off 35 You are clear on
this Yes Yeah Special Thanks This one you
have important Oh open city Yeah So yeah
that will come to it So the baby
are getting the images will come toe that
That's what I'm saying We'll do this We'll
redo this case story Indeed A Okay so
it's supposed to be ah mixed between week
in week 21 victory actually be very heavy
if I go through it right away OK
so we do this but I just wanted
to give you the idea off transferred learning
This is how we do transferred land Majorly
I don't waste my time doing this politically
Pull out the data The only drawback off
transferred learning is first thing is you have
You have to I will say completely dependent
on their weights tomorrow If they remove the
stuff from online or their support we are
helpless The model we go down And second
issue is that if the lady is not
trained on that particular set of images and
say you want o build a new network
on space images we should have some network
retrain on Patrick If it is not trained
on that then we not be able to
use That's only British use Otherwise everything works
Oh okay Perfect Yeah So this other two
we'll redo this I will We will go
through again And if possible I will Well
you life According so that we get hands
on to the complete quoting part Yeah And
also I want to give you the complete
flow So what is the standard flow offer
Ah CNN And what do we do What
should we do in our despite Okay this
is one thing Now let's come dosomething onto
our tow including So if you remember that
I mentioned reduction technique that will start talking
about every simple cord where we have the
same amnesty data handed and data on this
is the original image And this is the
under sampled image But I will say the
diamonds to live in Houston Good How do
we do that Get the library's get their
data reshaped the data Normalize the data Decide
that from this level of import How much
you want to come down To say I
want to come down to 64 depends on
you How much you want to reduce that
I mentioned Okay so once I come down
so that you do all the re sizing
while reshaping thing on the image part of
it next we have to call our model
So if you observe here where do we
have We have Gordon input So I just
explained you guys what is on the order
in quarter This is your real input Then
you will have some in being part of
it and then you allow some recording This
is according this decoding and this is your
own Okay so there's my input This is
my including less is my decoding less And
this is my auto import of this If
you observe here I have I'm not writing
multiple s I'm just writing normal dense less
and other things I think are self explanatory
Do you waste So this is another alternately
off creating a can us men small four
lengths enough after that What are we doing
is we're calling our backdrop and fitting the
mortar Now I'll fitting the model What are
we doing We're increasing the bad size we're
having some e parks there in shuffle is
equal to shuffle In the sense if we
if we wanted to do some commendation part
of it and destined brain split we're doing
here we are saying 7 80 20 split
Okay The validation is nothing But our testing
did when I ran it True And if
you look at the accuracy part I get
around Uh okay that isn't accuracy And I'm
really sorry because I did not We did
not put the metric over Here is accuracy
I just wanted to see the law spot
How much loss We're having An actual observed
The last person date is not that great
between No how we see this image How
do we officer whatever your land is good
or bad in that guests will call the
AAA auto in quarter or predictor indoor testing
data and then finally will block the original
later were sister Testing is also the only
showstopper here is I am not that you're
supposed to know this Okay How a matrix
gets converted to an image I am not
sure Symbolism and worked shape and size You
want me when you've been specified So here
I've got to India Cross training It is
the original emission is the dimensionally reducing it
Now if we are not happy with this
or let us say if you are making
ah implementation where when the war security guard
legacy runs the device through some of the
costs they will get the number plate automatically
or by looking at the low they will
get the make off the car or by
looking at the car will get the color
of anything here You don't need HD image
but you don't have to compute or spend
a lot of time and money and hardware
under this review the image and then compute
optics This could be a very simple application
Okay so this is your argument But there
is one more dimension reduction technique I'm not
sure if you guys a court about it
Have you heard about PS any No I
don't supervised learning No Yes I know No
Right Okay Let me know if you guys
are interested I would show something on this
also in the power Of course Anything But
I feel it's little importance on times So
let me show you first of all why
we need to Yes any and very in
all in deep learning you will need it
so something off the blacker Not not in
order The part Of course This is what
we do in industry I don't want us
to miss it See I have morning number
Ah mahn Yes started Okay so it is
a part off NLP So I show something
short Something extracted some of my matches Now
if you look at computer vision we saw
today water transfer learning and didn't that is
vtg Imagine it's election A leaner same thing
You can do it in your NLP als
where the honoree house some kind off including
available ready made So the first including that
we can see here is glove Another one
could be um you say Elmo another one
could be worked awake So where do it
is developed by Google So these are standard
embodies that we have will directly important and
start using is what we mean by plans
for doing so again It's an example So
what if I say that in our text
So what is this case story about As
we have got a huge text available in
us Now I want to know that uh
let us say uh I want to predict
something So I will say the prince to
raise friends will be tomorrow's question mark Okay
So what What we can do out of
this I will say today it is friends
is tomorrow's Nash What is it So obviously
you guys well know Prince is nothing But
tomorrow he'll be taking What if I remove
the prince and said Today's princess or today's
ah King Queen will be tomorrow's what we
don't know So this is used when you
want to do some kind of prediction Now
they the best example I'll give you is
linguine So when you are messaging when Lyndon
us have observed now So for example if
Parrish sends Minnis message on Lyndon something so
it'll give me some kind off tokens If
you're seen it it will first say hi
Harish for the morning Harish Sure Depending on
the data are what is return under If
you also protect me sure will meet tomorrow
or shoulder I will do that or something
How do we get these predictions Use something
called Caroline Okay Usar intend to do this
now what is Iran in its a different
network Recurrent Your little will do in NLP
anyway but aren in orderto uses or we
can sometimes or to use Club Glover's Ah
the way you did Regional in CV in
NLP we can say ready made a network
So this is called Global Victor's face and
it was developed by Stanford University Now what
in this What I'll do is I'll show
you What if I want to predict what
is the nearest according to king So from
our corpus remember What is corpus I assure
you over chat board corpus is a set
off huge data available to play in your
model Okay now what if I say give
me the top five words 1 to 6
minutes Five words which are very new thing
So if you observe Queen Monarch Prince Kingdom
and rain from our corpus is able to
find these other five predictions Which one you
want me to since I will say share
the top three predictions always something like a
recommendation system against for text All right now
why do I am I'm showing you this
because here if I want to re present
this what if this this This is all
back And I'm sure you basil them Have
our district borders their data What is it
What is he talking about If I want
to re present this what if I present
my tokens from my corpus able to visualize
this Not very good but you are able
to see that Yes There are some words
we took tightly packed That means they're very
near to each other There are some words
which are almost of a but he has
the an era So if you can order
a university in college okay School and education
hospital and health You can observe that these
words are tagged together Now if you ask
me how that's again A different topic How
I tack them together but glove will help
me to do it Okay so how I
re present Did this how relate Convert Ah
higher dimension imaged were to the image like
this We use PSN You're there So let
me go through tsa What if he is
sending a very simplistic way It is a
supervised learning dimensional reduction technique So far we
have been unsupervised learning dimension direction technique That
is specie right In this case what will
do us We will say there is a
truly data and we have got four points
they want do 34 I will say this
point Nobody at this point That would be
point Embassy point somebody Now I will send
a distance between again like me not give
the answer Now I will ask you ways
What are the similarity measures that you have
seen so far Based on how I can
find similarities Distance time similarity similarity and distance
walks immediately Okay In this case let me
let me let me say that I'm saying
distance would be one of the similarity matrices
So I will say for our convenience I
will say each one of them is that
a unit distant from each other and a
diagonal elements will be year Distance elsewhere or
cough two from each agreed Fight of Austria
Now how would I would use the diamond
ship Let me ask you I want to
reduce the dimension to one the house Right
So to do that Okay let me not
ask you Let me show you something Then
I'll ask you to do that when I
will lose And a couple point of reference
Let us sail Start with a I'll put
a wager now next to a what do
we have We have be our study We
have be here at that unit distance and
we have be it a unit distance And
now if I say their distance between a
B and B So now we are kind
off close If I keep distance in my
mind and if I replicate them on a
one day space I can transform them like
this Now please remember how I might transport
I'm transporting I'm saying that I will make
sure that I don't change their distances And
right now the similarity matrix between all of
them is distance So I have made in
distances between a B and B agreed Everybody
agrees to that Now what about where should
I place my seat What if I place
it here Because if you remember the distance
between DNC it's one And the distance between
E and C is also squared off too
So if I place it here if this
is C what is gonna happen B and
C is going to be far away right
As BNC going is kind of one in
Sofia So what if I bless See here
Now it's satisfied But now the will be
an issue So is anyway satisfied with both
of them but DnB air creating some issue
Percy So this is a problem if we
take this configuration Okay this brings us to
the end of this computer Which in tutorial
Now before you guys sign off I elect
inform you folks that we have launched a
completely free learning platform called Great Learning Academy
They have access to free courses such as
a iCloud and distill marketing So guys thank
you very much Fred and in the session
and have a great
