Discussing the article: "Neural Networks in Practice: The Perceptron"

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Check out the new article: Neural Networks in Practice: The Perceptron.

In this article, the perceptron is presented as the basis of a neural network, and its implementation in MQL5 is described in detail. We will explain what activation functions and their derivatives are, the difference between forward propagation and backpropagation, and how the least squares loss function and the gradient are used. You will learn how to train weights and biases, verify the results using test scripts, and adjust the learning rate to achieve stable convergence.

Although breaking this material down into parts is no easy task—since the topic requires familiarity with a vast number of elements and concepts, many of which are entirely abstract, while others are quite complex due to the use of the same terms in different contexts—I have tried to present the material as clearly as possible.

Among these concepts, the terms “backpropagation” (BACKPROPAGATION) and “forward propagation” (FORWARD PROPAGATION) stand out; they are typically used indiscriminately, without a proper explanation of their meanings. To fully understand everything that happens in the code of a perceptron network, we need to understand how these concepts came about.

For now, we won't go into certain details. However, we need to understand how the term “neural network” came about. To do this, we need to understand its foundation, known as the perceptron—which is the main topic of this article.

Author: Daniel Jose