Hi and welcome to chapter seven point one way introduce comp concept of convolutional neural nets basically
CNN's I'll be referring to them Truthout discourse so why is needed.
We spent a while discussing neural nets previously and maybe wondering why would we have spent so much
time on your own nets if we're not just going to suddenly discard them and go into CNN as well understanding
your own that's critical at understanding CNN's as they basically form the same foundation for all deeply
wrong types of networks all of them basically require you to understand stochastic gritty and descent
back propagation to training process botches iterations all of those things.
Basically CNN is just a different form of neural that's And you'll find out why and how and why.
So why CNN's because mainly because neural networks don't skill well to image data.
Remember you know intro slides we discussed how images are stored which was basically this.
You have a grid let's say this is a 10 by 10 grid here.
So we have 100 inputs here technically and each input has different colors.
So a lot of it is data and decrease your vision of this as well.
Our job would just be this.
So as I said neural nets don't skill well to image data.
And why is that.
Let's consider an image called image a small image 64 by 64 pixels.
So how many how many inputs is that 64.
By 64 times tree that's twelve thousand inputs already for what is a tiny image here.
So if all input lead will have at least twelve hundred twelve thousand weights and that is large.
And basically if we even go higher.
We have ninety nine some thousand weights in the piddly loon and that's not even considering how many.
How many more promises we have in the Himalayas as well.
So we need to find basically CNN doesn't actually reduce the weights in the beginning and the end.
But what it does do it [REMOVED] finds a representation internally in hidden layers to basically take
advantage of how images are formed so that we can actually make a neural net much more effective on
image data.
Let's see how this is.
So introducing CNN here this is effectively what it is here.
Oh I'm going to go to each of these in detail into little slides.
But for now conceptually So this is what it is.
Remember in neural nets the head in Podolia and hidden layers.
Well these are the hidden layers in a in a convolutional and that's what happens first is that we have
what is called convolution where Realto activation unit is applied to the convolution here.
And this convolution here it slides across image here producing values here.
Don't worry if you don't understand this just yet.
I'm going to go into detail in each one later on.
So just feel free to just basically look at this.
Get familiar with the terms and diagram how it looks.
But you don't actually have to know what these are yet.
So this is what convolutional neural nets look like.
So why should you leave.
I didn't mention truly is here before.
But you can see here that there are depths of Leia's stacks going to sway in this way.
Whereas New York and that's represented in north a flat diagram here.
So is allow us to use convolutions that least image features and by living image features you'll get
and so on what images are very soon.
And therefore this allows us to use Folles we you know deep that look allowing for significantly faster
training and a lot less parameters to.
So this is an arrangement of how neural nets use truly Vol. 1 am and when I said truly volume I'm referring
to the input to being in three dimensions here because we have height wit an adept call it up here.
This is for color image here.
So input go back to it here is effectively a true dimensional input that is fed into here.
And these are all in different dimensions as well.
So just effectively go is a as often for CNN.
We have the input layer or convolutional there are real new layer pooling and fully connectedly are
here.
That's it basically they can get more complex later on.
But these are the Corley's of CNN.
So why is it called CNN.
Well it's all.
Liam convolutional live here.
That's this one here also and this one here comes in sequences as well you can have.
This is how we adapt to CNN by having multiple stacked convolutional layers.
Now convolution is what allows us to actually learn image features.
And I will explain to you again what image features are.
But basically what I would classify uses to sort of detect what's in an image but what exactly is a
convolution.
Well let's find out and chopped up to 7.2.