mql5

Our experiments have confirmed the effectiveness of autoencoder models. Pay attention that we used fully connected neural layers to train autoencoders. Such models work with a fixed input data window. The algorithm we have built can training any models operating with a fixed input data window. But the architecture of recurrent models is different. To make a decision on the activation of neurons, such models also use their previous state, in addition to the initial data. This feature should be taken into account when building an autoencoder.

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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