First thing, AIDS, we're going to generate some data and some samples in this example, I'm going
to use Sina's function and I want to train my neural network to give me the same output as seiners function
before assorting.
Let's just create a folder here.
I already created a folder if I can find it.
Matlab finds it's better to just keep all the data in one file and not confusing later with other files
and folders.
So let's start here.
X equals linear space in space is a very dramatic progression and it can accept three different fields.
The first field would be deliver a line.
I'm going to set it to zero.
The next field would be the upper line.
Let's just go to two pi in Matlab.
You can simply type P as a pie.
And then how many samples do we need?
Let's start with tweeny samples.
Click, enter.
So here I can see the generated data that we have 20 samples from zero to two PI.
So it just divided from zero to two pies to any parts.
And here I can see it now.
Next one would be over output y.
I will define Y as a sign of X again.
If I just enter I can see the output of Y, let me do it again.
I'm going to type ACLC to clean the board and I can use page up and page down to go to the code which
I already wrote.
So here I'm gonna go and generate X with a semicolon at the end to make it clear and then again generating
Y this time again with semicolon.
So I won't see the digits.
Now it's time to plot plot X and Y and let's see what would be the output.
So we're looking for another window to pop up.
It's going to take a while.
OK, here it is.
Let's check it out here.
We have twenty samples.
If you zoom in, you can see this is not a curve.
It just feeds a line and just connect all the samples here.
You can see all the samples are just connected to each other.
And this is not a curve.
I want a neural network to fit a curve here.
But for now, if you want to point out to your data, you can type plot X and Y and then here use A
minus O inside, OK?
And so I just want I'm going to use minus and then letter O close apprentices and let's check it one
more time.
This time I can see my samples better.
And if you count them, if you don't trust you can just count.
There are twenty samples that I have now let's back here.
I already have the data I just generated twenty samples out of my output is sign yourself x now let's
train over neural nets for and see how kind of neural networks give us the same output.
Assign us of X for this purpose.
Please go to your APS.
This field is giving us several tools to use when you are working with artificial neural network.
Here we have classification linear as we have curve fitting, we have neural net clustering.
But let's start with neural net fitting.
You can also click on a star next time.
I can see it here at the top of my favorites, so just click on it.
There is another way to open this page here.
I can see an F two, so let's just click and close it and down and F to type it here.
You can already see this window.