Here is our exclusive logical Agard explorer, and these are our inputs.
This is a graph based on our inputs, which are X and Y.
As we can see on this graph, we cannot separate them with only one line, they are not separated.
Well, with line one line, there is no way to do that.
So we are required to use two different lines.
And we also learn in our artificial Narron that each line with each equation is representing one neuron.
So how can we show it with the artificial neuron if we have two lines?
In order to do that, let's just transfer them first on there or and and environment here.
I'm just transferring them.
We just took it and and all of them.
And this is how they look like now.
They are trackable, they are separate to bowl and we can already separate them with one line and this
line we can write an equation for it.
Let's say if this is Z2, we can show this is C one plus here is just passing from let's say if this
is one, then it would be zero point five Z, one plus zero point five, meaning we can write an equation
for that and we can represent it with one zero.
So let's take a look at this block over there.
These are of our inputs.
The thing which we did here was first take the add in over of them in previous sessions, we already
saw with order equations for and and or and how can we show them with a simple neuron.
Now they are moving to another layer.
This layer is our entry.
Zebb one here we have two lines, each one and Z2 each one is X and Y Z to ease X or Y and finally we
will have a Z three.
We can show that this Z tree is equal to X, X or Y or simple.
We we can already united as let's say, transfer all of them in one sine Z to minus one was zero point
five.
We D, X or gate.
We saw the problem and we also provide a solution, a practical solution for it.
This is a simple example of multilayer Perceptron, which we know he does MLP.
We have this a structure and we will able to classify this a structure by making it into different layers.
This is a basic fundamentals for multilayer perceptual.