But let's have a model for a simple artificial Nahrin here resolver example about some patients which
we want to see if they are diabetic's or not.
In this example, we just a function for classification exposed behind plus equal to zero.
Our inputs here are x1 x2 x3 through X often.
In reality, there are more than one parameter to help us making decision for a classification.
So it's not only one factor here.
There are actually more elements that have some effect on our decision for drawing this classifier of
line.
But you might also ask, so what happened to the B here?
We have B of Y.
B is an element which doesn't have any affect in our decision.
Let's say me, is how tough the patient.
It doesn't have really affect to our making decision for if the person is diabetic or not.
So some data, they don't have any effect on our output.
We have this Y here, but the weight of Y is just zero.
We don't consider it is just like V is zero.
If means equal to zero, then it means the effect of D Y in our output would be zero.
Take a look at this one here.
We have different weight for each input.
It means each input has a specific effect on the output and we show it with W one W two true.
W if I send all of them to a sigma and some sum of all of them would give us an output function.
But before that, here we have some other factor, a very important factor which we call lead by us
off of our system.
We can't show it with B or any other LATRA.
It's usually actually in most of the textbook.
It's just with B.
B is a standard for a bias of our system.
But why do we call it bias?
You have X, me Y plus C, what is the effect of C in our system?
If I set C to the zero then I will have a line off A X plus B Y equals two zero X plus B Y equals to
zero is just going from this line.
It should be somewhere around here.
So this is a straight line.
Here is our would be our line.
It means we're so strict, the doctor is so historic and this patient who was healthy before that,
we are now considering as a diabetic as well.
So D. C. is showing the bias of the system.
And how is Treweek is a doctor.
And if we just increase the amount of C, then we will have to line somewhere here.
It means all the patients now are healthy.
This bias will show how streak is over a system.
Of course, it has also some effect and we can show this effect by W zero and then some of all of them
is giving us an output function, which we call it usually as a activation function.
This is our net signal and that signal is giving us cell activation function that we just erase it all
here.
I can say they would be equal to F off net.
This next signal is equal over by us B plus.
Some way, W one, x one, and here we have W two, x two plus just goes to W of an X of N.
Or in other hand, we can say that the next signal is just equal to be plus the sigma of W i.
X of my.
Here is a model, a very simple artificial narrowing, which we know as my clock, Pete Neron, my clock,
Pete was the first model of artificial Neron and it's a very simple Neren to show our outputs.
Let's take a look at these actual Neron in this and we can say, oh, we can see also we have some inputs.
Here are our axes, the inputs.
Each of them has a specific effect on the output.
We show this effect.
How important?
Because some details are more important than other data.
So how we you show this importance.
We show it with W here.
We have different inputs.
Each of them has a specific rate.
And then here we do some process.
And finally after this process, we send a net signal to the output and here are our Z function and
our output.