Here we can show this single neuron with some inputs, with a set of inputs, which we can just represented
with a vector of X, and then each neuron has its own way, a specific way.
We can just show them with a vector of W.
And there is also a constant variable V, which we call it bias of our system.
And then finally, this one will give us some output.
We have also some function, which we call it a function of net care.
We have a function of net which equals W one, X one because of our inputs.
It's just the picture of inputs.
So they can be several inputs based on baseline of our data.
W one x1 plus W two x2 plus let's say W off and.
Xin.
And of course, we have a constant number B now, we can surely win metric, you can say that these
are W one, W two to W Alphen, and then here we have another matrix, which is X one, X two up to
X of.
Plus.
Will meaning here we have horizontal metrics multiply by vertical metrics and in metrics, we know that
this one would be X one times W one W two times X two and finally W and times X of it.
And here we already know that our X is a vector of X, we can just shoot, we need X one x one X to
up to here X and the same with overweights of our W which shows how effective is some input and what
is the effect of each input on our output.
W one W two to w off and we can already write it down as a W transpose because we have here to transpose
this one is horizontal.
Whatever matrix is a vertical so that we transpose X plus B and overall two, which is easy here.
Now we can write it as Z equals F of net and here this net signal would be F, W, transpose X plus
B, we just showed our output of this Neron by using a function of W transform, which is our weight
vector and X which is our input.
And finally, plus a constant number with a simple mathematical function we can represent over Narron.
Let's take a look at this and get and see how can we just show that when a new formula that we just
found out here we have and this is MASN y axis and X axis, these are over imputes and these circles
are representing zero for output or let's say totally false in these.
Drongo is showing one for outward or totally wrong.
And we already learned that we can separate them with a simple line.
This is an equation for this line and each one is representing one.
Let's take a look at this equation one more time.
We can have two output.
Z is equal to here, X and Y, and it can have two output.
This is either one or zero.
This is one whenever X plus Y minus one point five, greater or equal than zero.
Here is part these are above one.
And for this one, the Z can have output of zero whenever X plus Y minus one point five is less than
zero.
But we're looking at this function and by looking at this equation, what can you remember reading?
This is pretty clear.
This is over a step function graph of T, which has two output, one or zero.
Whenever a cheese grater equal, then zero.
This is one.
And whenever T is less than zero, that's zero.
So meaning we can write it already Z equal to F of T and here our T is the net function.
So we can also write it as F of X plus Y minus one point five.
So meaning eliminate magical view, we can simply show and Narron with this function.