Conclusion

We have reached the end of the book. Throughout its pages, we have explored some of the most popular architectural solutions from various domains. These include convolutional models used in image recognition tasks, recurrent models for processing temporal sequences, and the Transformer with the Self-Attention mechanism, developed for solving language-related tasks.

My intention in showcasing these diverse architectural solutions wasn't merely to provide examples. It's a reminder to never fear to experiment. While it's easier to follow the beaten path, it only leads to repeating what has already been achieved, no matter how good these achievements may be. While there is nothing inherently wrong with this, true innovation and personal growth come from venturing off-road and embracing the unknown. The outcomes of such journeys are uncertain as they may lead to acclaim and success or fade into obscurity. Yet, I firmly believe that every effort contributes to our growth. As you move forward, I hope you find success in achieving your goals.

In this book, we have built a library that will assist you in implementing your own neural network models, training them on historical data, and testing their performance in the strategy tester using the provided Expert Advisor template. I wish you to find the model that will bring you profit and prosperity. It is important to remember: Make sure to thoroughly verify and comprehensively test the Expert Advisor before entrusting it with your savings.

See you soon. You can always find more information on mql5.com website.