mql5

We continue to study the Transfer Learning technology. In the previous two articles, we created a tool for creating and editing neural network models. This tool will help us transfer part of the pre-trained model to a new model and supplement it with new decision layers. We will also check the usability of our tool.

more...

---------------------

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