Now, we have finalized all the lawyers for CNN.
Now we are here to sum everything up, usually because this is deep learning, it makes perfect sense
that we add multiple layers.
Let's say here we are adding to convolutional layer with their activation function before we add one
polling layer.
Again, we are doing this multiple times than we are already.
Full layer again here and then we are hoping, you see, which is the fully connected layer in order
to predict the class.
OK, so actually the way you divide these depends on the architecture.
Now there is a lot of architectures out there that you can use.
There is net 101, there is a net 50.
There is Viji 19.
There is Visagie 16.
There is Alex Net.
Each one has its own configuration.
So right now, whenever you hear the term DNN architecture, you know what they are talking about.
They are talking about how these layers are connected together, how many levers we have.
This is all about CNN actually.
Right now you are ready to go and start building this and we will do this together in the upcoming lectures.
Now, let me just summarize this pretty quickly.
We are giving an image of a car.
We are passing it to a convolution.
Then we are passing an activation function to it.
As you can see here, some of them are black.
Why is it the black?
Because maybe we have some negative values that don't zero.
This is why we are getting some black values right here.
Next, we have another convolution.
Then we have Bull, which will extract the most distinct features.
Then you take those distinct features and you go deeper.
You try to control those again.
Well, Convolve, bring those segments will give you some features that you as a human want to be able
to recognize.
But the computer will do a great job recognizing them.
So maybe you will recognize the stuff up until this point, you will see what are the features that
the CNN is extracting.
But he deeper in the net, it will extract some really weird textures, these textures.
You won't be able to even understand what they are about, but the model will be able to understand
what they are and it will help a lot in the final connected layer and the fully connected layer in order
to predict the class.
Here we are training five classes, cars, trucks, airplanes, ships, horses, and as you can see,
it give a small possibility for a truck, also for airplane, almost a zero possibility for a horse,
but a very large possibility for a car.
So CNN is nothing but a probabilistic model to tell you how close is this to what it's predicting.
And it does a great job and is classifying an ignition.
Now, next section, we will go to our rescue by we will go to our python.
We will install some libraries.
I will introduce you to the most important libraries.
And then we are going to start building our model together.