mql5

We continue our immersion in the world of artificial intelligence. What is Transfer Learning and why do we need it? Transfer Learning is a machine learning method in which the knowledge of a model trained to solve one problem is reused as a basis for solving new problems. Of course, to solve new problems, the model is preliminarily additionally trained on new data. In the general case, with a properly selected donor model, additional training runs much faster and with better results than training a similar model from scratch.

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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