mql5

We continue to study model training algorithms. All the previously considered algorithms used an analytical method for determining the direction and strength of changes in model parameters during the learning process. While the methods to solve these problems turn to be inefficient. In such cases, we resort to evolutionary optimization methods.

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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
15. Neural networks made easy (Part 15): Data clustering using MQL5
16. Neural networks made easy (Part 16): Practical use of clustering
17. Neural networks made easy (Part 17): Dimensionality reduction
18. Neural networks made easy (Part 18): Association rules
19. Neural networks made easy (Part 19): Association rules using MQL5
20. Neural networks made easy (Part 20): Autoencoders
21. Neural networks made easy (Part 21): Variational autoencoders (VAE)
22. Neural networks made easy (Part 22): Unsupervised learning of recurrent models .....
23. Neural networks made easy (Part 23): Building a tool for Transfer Learning
24. Neural networks made easy (Part 24): Improving the tool for Transfer Learning
25. Neural networks made easy (Part 25): Practicing Transfer Learning
26. Neural networks made easy (Part 26): Reinforcement Learning
27. Neural networks made easy (Part 27): Deep Q-Learning (DQN)
28. Neural networks made easy (Part 28): Policy gradient algorithm
29. Neural networks made easy (Part 29): Advantage Actor-Critic algorithm
30. Neural networks made easy (Part 30): Genetic algorithms