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

Hebb's rule

Hebb's rule has been proposed as a conjecture in 1949 by the Canadian psychologist Donald Hebb to describe the synaptic plasticity of natural neurons.

A few years after its publication, this rule was confirmed by neurophysiological studies, and many research studies have also shown its validity in many applications of artificial intelligence. Before introducing the rule, it's useful to describe the generic Hebbian neuron, as shown in the following diagram:

Generic Hebbian neuron with a vectorial input

The neuron is a simple computational unit that receives an input vector , from the pre-synaptic units (other neurons or perceptive systems) and outputs a single scalar value, y. The internal structure of the neuron is represented by a weight vector, , that models the strength of each synapse. For a single multi-dimensional input, the output is obtained as follows: