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Hi and welcome to chapter seven point one way introduce comp concept of convolutional neural nets basically

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CNN's I'll be referring to them Truthout discourse so why is needed.

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We spent a while discussing neural nets previously and maybe wondering why would we have spent so much

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time on your own nets if we're not just going to suddenly discard them and go into CNN as well understanding

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your own that's critical at understanding CNN's as they basically form the same foundation for all deeply

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wrong types of networks all of them basically require you to understand stochastic gritty and descent

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back propagation to training process botches iterations all of those things.

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Basically CNN is just a different form of neural that's And you'll find out why and how and why.

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So why CNN's because mainly because neural networks don't skill well to image data.

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Remember you know intro slides we discussed how images are stored which was basically this.

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You have a grid let's say this is a 10 by 10 grid here.

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So we have 100 inputs here technically and each input has different colors.

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So a lot of it is data and decrease your vision of this as well.

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Our job would just be this.

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So as I said neural nets don't skill well to image data.

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And why is that.

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Let's consider an image called image a small image 64 by 64 pixels.

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So how many how many inputs is that 64.

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By 64 times tree that's twelve thousand inputs already for what is a tiny image here.

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So if all input lead will have at least twelve hundred twelve thousand weights and that is large.

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And basically if we even go higher.

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We have ninety nine some thousand weights in the piddly loon and that's not even considering how many.

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How many more promises we have in the Himalayas as well.

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So we need to find basically CNN doesn't actually reduce the weights in the beginning and the end.

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But what it does do it [REMOVED] finds a representation internally in hidden layers to basically take



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advantage of how images are formed so that we can actually make a neural net much more effective on



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image data.



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Let's see how this is.



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So introducing CNN here this is effectively what it is here.



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Oh I'm going to go to each of these in detail into little slides.



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But for now conceptually So this is what it is.



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Remember in neural nets the head in Podolia and hidden layers.



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Well these are the hidden layers in a in a convolutional and that's what happens first is that we have



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what is called convolution where Realto activation unit is applied to the convolution here.



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And this convolution here it slides across image here producing values here.



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Don't worry if you don't understand this just yet.



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I'm going to go into detail in each one later on.



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So just feel free to just basically look at this.



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Get familiar with the terms and diagram how it looks.



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But you don't actually have to know what these are yet.



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So this is what convolutional neural nets look like.



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So why should you leave.



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I didn't mention truly is here before.



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But you can see here that there are depths of Leia's stacks going to sway in this way.



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Whereas New York and that's represented in north a flat diagram here.



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So is allow us to use convolutions that least image features and by living image features you'll get



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and so on what images are very soon.



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And therefore this allows us to use Folles we you know deep that look allowing for significantly faster



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training and a lot less parameters to.



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So this is an arrangement of how neural nets use truly Vol. 1 am and when I said truly volume I'm referring



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to the input to being in three dimensions here because we have height wit an adept call it up here.



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This is for color image here.



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So input go back to it here is effectively a true dimensional input that is fed into here.



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And these are all in different dimensions as well.



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So just effectively go is a as often for CNN.



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We have the input layer or convolutional there are real new layer pooling and fully connectedly are



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here.



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That's it basically they can get more complex later on.



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But these are the Corley's of CNN.



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So why is it called CNN.



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Well it's all.



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Liam convolutional live here.



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That's this one here also and this one here comes in sequences as well you can have.



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This is how we adapt to CNN by having multiple stacked convolutional layers.



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Now convolution is what allows us to actually learn image features.



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And I will explain to you again what image features are.



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But basically what I would classify uses to sort of detect what's in an image but what exactly is a



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convolution.



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Well let's find out and chopped up to 7.2.