We all know they learn how to design and learn and why sometimes we need two layers of perception in
this session here.
We have multilayer perception in this example.
This is a word input zero input one.
The first layer, usually in many textbook, it's called input layer.
Then whatever the layer is between input schlager and output layer is called hidden layer, and they
call it hidden layer because that's hidden from the output layer.
And finally, we will have some outputs.
Let's take a look at better example here.
Here we have several layers and all of them are called hidden layers in Matlab as well.
All of them actually in Matlab.
We don't have input layer.
I just called all of these layers hidden layer.
But anyway, this one is a hidden layer.
And finally we have an output layer which will give us an output.
So let's see what we have here.
What are our parameters, we can change the weight of each neuron as well as our bias for the system.
So the parameters that we can change in this model are overweight.
And the bias.
And we need to adjust and change these parameters until we have the result, which we are looking for.
Let's check the example of different handwriting samples we are designing and neural nets for which
can read of our handwriting or different handwriting.
If the system cannot, for example, read this parameter, then we need to fill it with more data.
And also we need to adjust the parameters that we have here, including the weight as well as the bias.
So this is an optimization agreed to and we need to optimize we need to adjust different parameters
in order to have the best output that we are looking for.
Adjusting different parameters is called training.
So we need to train the system and we need to teach the system.
The system is learning based on different parameters and based on different inputs that we are feeding
into our network.
This is a structure of multilayer perceptual.