If you ever want to talk about deep learning in order to recognize images, you need to talk about convolutional
neural network or CNN.
Now, this is a very, very famous architecture for image processing that has emerged recently.
It's a new technology and it's very, very powerful.
And it beats all the conventional image processing techniques that we used to use 10, 20 and 30 years
ago.
So this is almost the replacement for all of these techniques, because if you remember, a neural network
or a deep neural network is a general function that can calculate anything.
Right.
So when you give it an image, if you do the right adjustments to your parameters, you could get some
really great results and classifying recognition.
So let's see how a convolutional neural network really functions.
So before we were using in the networks to classify images and we used also deep neural networks to
also classify images.
Now, those were really good if we have a small images because we don't need a lot of neurons in order
to do that.
I mean, we can fit for you, let's say five by five image needs, 25 input for the neural network.
Right.
We need 25 input nodes because every pixel needs one note.
Right.
So let's say that I have 64 by 64 or maybe 512 by 512.
If you multiply these two numbers together, you'll get around twenty six thousand input nodes.
And that's a huge.
This is a lot of computation power.
This is a lot of wasted time because it takes a lot of time.
Well, it's not really feasible for large images.
And you cannot have this high accuracy if your model is too complicated.
So what is the solution for that?
How can I not use one neuron or why one input neuron for every pixel?
This is not really feasible.
I need some other technique now.
A convolutional neural network is going to operate by breaking down your images into its basic components,
as we have seen in the example.
Now it will do the same for faces.
As you can see all of these faces, it will break them down to eyes, noses, mouth, cheeks.
It will break it down to all of these basic components.
It will also break it down to edges like the edge of your face, the edge of your nose, the edge of
your lip.
So it has edges.
It has more obvious high level features and then it has the face.
So it's very good.
And you're breaking down the face into very basic components so that it can compare it and say, OK,
this is the face.
This is not a call, this is a face.
Maybe it will take the basic components of the call and tell you, OK, this is a call.
If you give it an faced image, it will tell you, no, this is not a face, this is a goal.
But it uses different techniques than the neural network, as we're going to see.
Now, CNN has layers and every layer does a different thing.
We have convolutional layer.
We're going to talk about it.
Activation functions, borling layer, and it has fully connected layer.
So only the last layer is like the neural network we have talked about.
All of those are new concepts which we will learn in the next lecture.
We will go over them one by one.