Discussing the article: "Neural Networks in Practice: The Birth of C_Neuron"

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Check out the new article: Neural Networks in Practice: The Birth of C_Neuron.

This article demonstrates how to encapsulate a neuron in MQL5 using the C_Neuron class, with weights, a bias, and a parameter specifying the number of inputs. We will take a detailed look at how to compute the loss function based on the least squares method and how to organize training data in arrays. This makes it easy to change the inputs and repeat experiments without modifying the implementation.

Before we begin, there’s one thing we need to clarify, dear reader: the class we’ll create to represent a neuron will be very simple and straightforward for now. We will not include anything other than what is strictly necessary. The code will be as simple and clear as possible, although it will be inefficient in terms of execution time. Treat this time measurement as the execution time.

The goal here isn't to write efficient code, but to write functional code that anyone can easily understand. Even someone who is just starting to program in MQL5 and is interested in neural networks will be able to understand this, since we'll be using plain MQL5. I might add a little SQL in the future, but I'll try to stick as closely as possible to simple and clean MQL5.

Author: Daniel Jose