mql5

In this series of articles, we have already made a substantial progress in studying various neural network algorithms. But all previously considered algorithms were based on supervised model learning principles. It means that we input some historical data into the model and optimized weights so that the model returned values very close to reference results.
There is another approach to Artificial Intelligence learning methods — unsupervised learning. This method enables model training only using the original data, without the need to provide reference values.
In this article, you will not see the previously used vertical structure of a neural network consisting of several neural layers. But first things first. Let's consider the possible algorithms and see how they can be used in trading.

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1. Neural networks made easy
2. Neural networks made easy (Part 2): Network training and testing
3. Neural networks made easy (Part 3): Convolutional networks
4. Neural networks made easy (Part 4): Recurrent networks
5. Neural networks made easy (Part 5): Multithreaded calculations in OpenCL
6. Neural networks made easy (Part 6): Experimenting with the neural network learning rate
7. Neural networks made easy (Part 7): Adaptive optimization methods
8. Neural networks made easy (Part 8): Attention mechanisms
9. Neural networks made easy (Part 9): Documenting the work
10. Neural networks made easy (Part 10): Multi-Head Attention
11. Neural networks made easy (Part 11): A take on GPT
12. Neural networks made easy (Part 12): Dropout
13. Neural networks made easy (Part 13): Batch Normalization
14. Neural networks made easy (Part 14): Data clustering