<caption><image_8>The image is a screenshot of a webpage with a title "Back Propagation". The title is written in black text on a white background. Below the title, there is a diagram of a network of interconnected lines and dots, representing the relationships between different types of polygons. The lines are connected by arrows, indicating the flow of data between them. 

On the right side of the image, there are two bullet points that explain how to adjust weights for each type of polygony. The first bullet point explains that using these weights we can compute what the input to let layer should be, and the second bullet point provides instructions on how to use these weights to adjust the previous layers weights.

At the bottom of the page, there appears to be a brief description of the webpage, providing further information about the topic.</caption><caption><image_9>The image is a screenshot of a computer screen with a white background. On the top left corner of the screen, there is a text box with the word "Input" written in black. Below the text box, it reads "Need to map input into web browser". On the right side of the image, there are two small icons - one is a blue light and the other is a black dot. The image appears to be a screenshot from a video or a presentation.</caption><caption><image_10>The image is a screenshot of a computer screen with a white background. On the left side of the screen, there is a grid of black and white squares arranged in a grid-like pattern. The squares are labeled with numbers and letters, and the text above them reads "Input". On the right side, there are two rows of numbers, one labeled "1" and the other labeled "2". Below the numbers, there appears to be a text field where the user can input the input into the grid. The text field is blank, indicating that the user is in the process of inputting the input.</caption> and so back propagation is and then sort pushing the backwards now we've got a longer list of knobs we can turn now as if your mind people that basically careful or fitting we've got that many knobs you can turn you can start turning and then telling four times you get something that gives you a really good answer is put in a not fit the model one of bounds of network is as opposed say then your regression is a linear regression we can sort of it's a lower dimension sort of look at the data and CEO is this when you're not there on and it's hard to see explain on so there's a whole bunch of things we know you can the exhaustion will divide the hope that's in a few parts or kearney data validation attest and then we get this crazy workflow that you can take their surveys and run now if you actually want to do something with little networks we need to math input into a vector but we can pass it on to our networks not our neurons you know so if we're looking at
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