Here we have two systems, one is artificial neural network, which we want to design, the other one
is just the process.
It can be any process.
It can be the correlation between to a stock market.
It can be a doctor who wants to make some decision for a patient or any other processes.
Here we have our input.
We just feed the input into both systems.
And let's see what would be the output of an actual system, a real system and a system which we designed
using artificial neural network.
For this one here, we have a function of X, which is of our input based on our teta, which is of
our parameters.
We already discussed this parameter are the weight and Bias's number of layers which we can adjust.
Suppose here the output is why.
Let's call the other output y hat.
The best system is when Y is equal to y hat.
If we are looking actually for this result, we want to have a system, an artificial neural network,
which gives us the same output as other process.
But is it actually it's actually possible.
Let's check it out to you and describe here I have I and my hand.
If we plot them then all the data should be exactly in this line.
It means one is equal to Y and this is the best system.
This is ideal system that we are looking for.
In reality.
Usually this won't happen.
There are several reasons.
For example, if we have a natural system, usually it's nearly impossible to include all the factors
which have effect on our output.
For example, in moon can effect on our health.
So this is a parameter which doctors usually don't use it when they want to make some decision for their
patients.
But what if this factor is very important that can change the output, but we really don't know how
to use it in how to measure it so we don't include it in our artificial neural network.
So especially for the natural systems, it's nearly impossible to have the same output for Y and Y,
and that's why we are looking for minimizing this error.
If we subtract the output of Y from Y hat, then we should have our error.
So this is the error that we are looking for minimizing.
We are looking for minimizing.
Usually we use a or it can be minimizing the absolute value of it.
If it goes to zero, if E goes to zero, then that's the ideal answer and that is the ideal output that
we are looking for.
There are several ways that you can adjust your parameters to get the best result for the output.
That's why we need to train our system in order to minimize the error.
Let me just show it to you with the example.
Suppose this is your house and this is your workplace.
I can say there are infinite number of different ways to get from your home to your workplace and you
choose one of them based on the situation.
It's not usually the shortest, not always the shortest way.
Sometimes if you go, lets me just call them road one to three.
Sometimes if you choose route number three, then you can escape the traffic and you can get to your
workplace faster.
But there are infinite number of ways that you can get from your home to your workplace.
Not all of them are actually a good way.
But however, I can say there are infinite numbers of getting from your home to your workplace.
Let's look at this one.
This is another example.
This is a very not very ideal, but this is a way for our system.
It's also the same.
There are infinite numbers of ways that we can adjust the number of layers we can add.
Just a parameter is the way to biases, but only one of them is the best based number system.
We need to train over a network and we are looking to find the best way to minimize this error and to
have the closest result for why and why that.