We're 50 samples, I will call it X, one equals minus pays from zero to two PI and then 50.
Don't forget to put the semicolon here.
Ricki's and for Y let's call it real live one equals two signers of X one semicolon and then plot X
and Y, X and Y one.
That's it.
Now, we don't need this one, let's back to the waiting to hear.
We can simply click back and then choose your samples again this time X one and why one?
Click on Next the same, I'm just going to choose five layers like a previous one and click on train.
So here it is, you can see some information to mean square error.
And let me open this window.
It's order this time the gradient stop of our training, which means the gradient just went to zero
and then that was good enough.
We just stopped the training.
Let's check the performance.
OK.
Actually, this is very great.
Now that we have more samples over network was able to train itself better.
We see more examples, just like when you are teaching math, if you give your students more samples,
then they will have a better understanding and the chance that they can pass the test or the quizzes
is more.
Now we can see the result.
It's definitely a better training.
And let's check the training state.
Here is our gradient, which went to Z towards zero and we had the number of Époque actually.
It's very interesting.
Two hundred sixty four, it means two hundred sixty four times.
The network train itself adjusts the weights until it finds the best result.
Then it just stopped the training.
Let's see the error histogram.
Here it is.
It's all around zero except these two, the rest are just around zero and that's a very good error histogram.
Next one is our regression.
Let's check it here.
Just compare it to the previous one with twenty samples.
We can obviously see the improvement even for testing.
It's just closer to our goal, which is one Y equal to T.
That's good.
And finally, let's see, how was Overfitting?
Very well.
Defeating is just like a sinus wave.
This is what we expected from our neural network to give us a sinus wave.
And that's it.
We can see already the sinus way you train of our network with five measuring, this time with fifty
samples.
And as the result, we can see a better result than we can see a better training.
And here are other errors.
You can analyze it later if you need, but that's a very good outfit.
The other thing is, let's see what will happen if we give it more data, more samples like what will
happen if we have 1000 samples.
If you click on next here, you can choose different inputs and targets.
Let me go back to the command window.
So I'm going to define different samples.
This time.
X two equals two space from zero to two PI just same with previous one.
And let's go for one thousand data and see what will happen with one thousand samples.
And one two would be equal to sign this of course.
X to.
Plots and let's see the result, plot X to Y to.
Just go back to normal fitting to this time, choose X one, y two here, you can also see some information,
like one thousand samples of one element.
And here we have 1000 samples of one element, four y two.
Now I'm going to click and test network.
Here is my mistake or in the regression.
You can also see the plot.
Let's see if we can have some improvement.
However, the previous 450 sample, that was enough already.
We saw the sine wave, which we were expecting from all of our network.
Here it is in a separate window.
OK, that's that's also a better fit.
The reason that you can see it like a tick line is because you have 1000 samples.
So fitting all of them just gave us this result.
We can see some error histogram that's around zero and that's a very better training.
And for the past regression, it's almost actually one.
Now, the next team that we are going to learn is the effect of different layers on our network.
Let's see how the behavior off of our neural network will change if we set the different number of neurons
like one neuron.
If you tried with one neuron to number one five neuron and finally one hundred neurons, let's see what
will happen.