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Neural networks made easy (Part 17): Dimensionality reduction

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by , 08-05-2022 at 01:11 AM (5086 Views)
      
   
We continue to study models and unsupervised learning algorithms. We have already considered data clustering algorithms. In this article, I will explore a solution of problems related to dimensionality reduction. Essentially, these are certain data compression algorithms that are widely used in practice. Let us study the implementation of one of these algorithms and see how it can be used in building our trading model.
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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

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