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Mastering Machine Learning Algorithms

You're reading from   Mastering Machine Learning Algorithms Expert techniques to implement popular machine learning algorithms and fine-tune your models

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788621113
Length 576 pages
Edition 1st Edition
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Author (1):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
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Table of Contents (17) Chapters Close

Preface 1. Machine Learning Model Fundamentals FREE CHAPTER 2. Introduction to Semi-Supervised Learning 3. Graph-Based Semi-Supervised Learning 4. Bayesian Networks and Hidden Markov Models 5. EM Algorithm and Applications 6. Hebbian Learning and Self-Organizing Maps 7. Clustering Algorithms 8. Ensemble Learning 9. Neural Networks for Machine Learning 10. Advanced Neural Models 11. Autoencoders 12. Generative Adversarial Networks 13. Deep Belief Networks 14. Introduction to Reinforcement Learning 15. Advanced Policy Estimation Algorithms 16. Other Books You May Enjoy

Hebbian Learning and Self-Organizing Maps

In this chapter, we're going to introduce the concept of Hebbian learning, based on the methods defined by the psychologist Donald Hebb. These theories immediately showed how a very simple biological law is able to describe the behavior of multiple neurons in achieving complex goals and was a pioneering strategy that linked the research activities in the fields of artificial intelligence and computational neurosciences.

In particular, we are going to discuss the following topics:

  • The Hebb rule for a single neuron, which is a simple but biologically plausible behavioral law
  • Some variants that have been introduced to overcome a few stability problems
  • The final result achieved by a Hebbian neuron, which consists of computing the first principal component of the input dataset
  • Two neural network models (Sanger's network and Rubner...
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