OK, back to over Motlop, let's just not change them for now.
Later, I'm going to change validation and testing, but for now it's OK, 70 percent for training,
15 percent of samples, which here is three samples for validation and 15 percent for testing.
Now, just click on next in this board, we are going to add just a number of hidden layers.
But the question is why we can't just change the number of output layer, why we only have the option
to change the number of neurons in a hidden layer.
The answer is actually pretty obvious.
If you have more than one neuron in our output layer, it means we will have more than one output.
And the question is why do we need more than one output?
We have only one output.
If we choose to narrow, it will give us to output what we have on the one output.
So in this structure, we only need to have one neuron for the output layer.
However, it's different for the hidden layer.
We can change into different numbers for our training and I will show you the result with a different
number of hidden neurons in the hidden layer.
So let's just clear the next.
Now the network is ready to be trained.
We just divided the samples to training, validation and testing.
We can choose here in the training algorithm by default.
It is very clever.
We can change it.
Let's take a look at this explanation.
This algorithm typically requires more memory, but less time training automatically stop evangelisation,
a stop improving as indicated by the increase in the mean square error of their validation samples.
So it really depends on the type of data and samples that we have, but we have other options here.
We have this one, this chickweed here.
This algorithm typically requires more time, but can result in good generalization for difficult,
small and noisy sets.
So again, it's really important to know the type of data set and to understand what is each of them
for and then choose the best one to fit your data.
The next one is the gradient descent algorithm requires less memory training automatically a stubborn
generalization to stop improving as indicated by increase in Amena Square.
Or this is just in the matter of generalization look like 11 Berthot using less memory.
I'm going to just set it for one.
That's a start.
Training we can train.
It will take a little time based on the data that you have.
Our network now is training.
Sometimes if you have lots of data, it might take even several hours to train the network.
But for now I have only 20 samples here.
I have this is a neural network training information.
It just gives us some information about the training that we had.
We can call it any time into programming.
We do come in, we know with and train to.
These are some information about algorithm.
The data division was random.
It just select the data randomly and this is what we use most of the time.
Unless you have some data, for example, you have a parameter and it's very important for you to select
three samples from the beginning, three samples from the end and three samples from the middle.
You can later it in a programming map by default is random for selecting the data training.
It just use Lowenberg for training for the performance.
It's using Mosquera and the calculation is just using mix.
Let's take a look at this process.
The airport is just a number of times that it tries to train the network using the algorithm which we
chose each time.
It will change the rate based on this algorithm and then conduct a training process one more time.
It can go from zero to 1000 times.
It will stop the training process if it's reached one thousand time.
Next one is about the time we didn't set any time.
We can do it in a programming, but it just took two seconds.
If you have a big data, you can adjust this time and you can, for example, city train for three minutes.
And after we reached three minutes, stop the training processes or just.
Limited to several hours, if you have a complicated system and it requires lots of time for training,
you can just limit the time.
But in our example, and most of the examples actually really don't need to set the time and it's going
to be less than five minutes.
The performance issues of performance and error.
If error reached zero, then stop the training, because that's the best solution that we are looking
for.
Zero for the error.
Next one is the gradient.
Just to stop the training whenever the gradient rej one over negative seven.
But why not to zero?
Because we know that in derivative, if derivative reached a flat point, then that's it.
We need to stop the training and that's a result that we are looking for.
Here we have Ammu and last one is validation checks.
It has different color, it's green and it's reached the maximum elevation check.
Each time the validation fails, we will give it one.
And then if six times we have a fail result for the quizes that we are conducting during the training,
then there is no point to continue this training and we are just getting over train result.
So we have to stop the training and each item which reached its limit.
First, it's going to stop the training processes and it will be in a color of green here.
We can also see here validation is the.