Architectural solutions for improving model convergence

We have discussed several different architectural solutions for neural layers. We have created classes to implement the discussed architectural solutions and small models using them. However, in the process of studying neural networks, we cannot bypass the issue of improving neural network convergence. We have considered theoretical aspects of such practices but have not yet implemented them in any model.

In this chapter, we dive deeper into the construction and application of architectural solutions improving the convergence of neural networks, such as Batch Normalization and Dropout techniques. Regarding batch normalization, we start by examining its basic principles, then proceed to a detailed discussion of creating a batch normalization class using the MQL5 programming language, including forward and backward pass methods, as well as file handling methods. The section also covers multi-threaded computing in the context of batch normalization and provides the implementation of this approach in Python, including the creation of a script for testing. An important part of the discussion is the comparative testing of models using batch normalization, showing the practical effectiveness of the approaches considered.

Moving on to the topic of Dropout, we will examine its implementation in MQL5, including feed-forward, backpropagation and file handling methods. We will see multi-threaded operations for the Dropout mechanism and its implementation in Python. As we close this chapter, we will conduct comparative testing of models using Dropout and see the impact of this technique on the convergence and efficiency of neural networks. Thus, we will not only study the theoretical aspects but will also practically apply them to improve the performance and convergence of models.