You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

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Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

5. Graph-Based Semi-Supervised Learning

6. Clustering and Unsupervised Models

7. Advanced Clustering and Unsupervised Models

8. Clustering and Unsupervised Models for Marketing

9. Generalized Linear Models and Regression

10. Introduction to Time-Series Analysis

11. Bayesian Networks and Hidden Markov Models

12. The EM Algorithm

13. Component Analysis and Dimensionality Reduction

14. Hebbian Learning

15. Fundamentals of Ensemble Learning

16. Advanced Boosting Algorithms

17. Modeling Neural Networks

18. Optimizing Neural Networks

19. Deep Convolutional Networks

20. Recurrent Neural Networks

21. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

26. Other Books You May Enjoy

27. Index

Summary

In this chapter, we have discussed Hebb's rule, showing how it can drive the computation of the first principal component of the input dataset. We have also seen that this rule is unstable because it leads to the infinite growth of the synaptic weights and how it's possible to solve this problem using normalization or Oja's rule.

We have introduced two different neural networks based on Hebbian learning (Sanger's and Rubner-Tavan's networks), whose internal dynamics are slightly different, and which are able to extract the first n principal components in the right order (starting from the largest eigenvalue) without eigendecomposing the input covariance matrix.

Finally, we have introduced the concept of SOM and presented a model called a Kohonen network, which is able to map the input patterns onto a surface where some attractors (one per class) are placed through a competitive learning process. Such a model is able to recognize new patterns ...