All about MQL5 Wizard : create robots without programming. - page 6

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 31): Selecting the Loss Function

MQL5 wizard can be a test bed for a wide variety of ideas, as we have covered so far in these series. And every once in a while, one is presented with a custom signal that has more than one way of being implemented. We looked at this scenario in the 2 articles about learning rates, as well as the last article on batch normalization. Each of those aspects to machine learning presented more than one potential custom signal, as was discussed. The loss , also by virtue of having multiple formats, is in a similar situation.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 32): Regularization 

Regularization is another facet of machine learning algorithms that brings some sensitivity to the performance of neural networks. In the process of a network, there is often a tendency to over assign weighting to some parameters at the expense of others. This ‘biasing’ towards particular parameters (network weights) can come to hinder the network’s performance when testing is performed on out of sample data. This is why regularization was developed.