Discussing the article: "Neural Networks in Practice: When to Use an Artificial Neuron and How It Works in MQL5"

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Check out the new article: Neural Networks in Practice: When to Use an Artificial Neuron and How It Works in MQL5.

We implemented a neuron in MQL5 that was trained using stochastic gradient descent and compared its loss function to that of linear regression. Using code and graphs, we demonstrate how normalization, the choice of learning rate, and the structure of the problem affect convergence. This article provides guidance on debugging the training process, interpreting error behavior, and selecting the appropriate architecture or activation function.

Judging by what the media, influencers, and others have been saying for some time now, it seems as though we won't be able to live without neural networks. As if they were the eighth wonder of the modern world. Actually, that's not quite how it is. As you may have noticed from previous articles, the study of neural networks is a very interesting field of research. However, in general, they have limitations and can malfunction. They are not capable of solving certain types of problems and are not as versatile as many people claim.

In many cases, a program designed to solve a specific problem will be significantly more efficient than any neural network we could create. This is due to several factors. If you, my dear reader, decide to use a neural network for a particular task, you'll have to train it so that it can perform that task. In addition, it may be the case that it won't even be able to interpret the data you're using to train it.

I haven't mentioned this yet. Although it isn't always necessary, in most cases you'll need to normalize the values that the neural network will use. We'll get into why later, but you should keep this in mind. Sometimes you'll need to normalize the input values or scale them to a specific range. If this is not done, the neural network will not be able to converge and form an adequate representation of the training data.


Author: Daniel Jose

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