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 presented the MRF as the underlying structure of an RBM. An MRF is represented as an undirected graph whose vertices are random variables. In particular, for our purposes, we considered MRFs whose joint probability can be expressed as a product of the positive functions of each random variable. The most common distribution, based on an exponential, is called the Gibbs (or Boltzmann) distribution and it is particularly suitable for our problems because the logarithm cancels the exponential, yielding simpler expressions.

An RBM is a simple bipartite, undirected graph made up of visible and latent variables, with connections only between different groups.

The goal of this model is to learn a probability distribution, thanks to the presence of hidden units that can model the unknown relationships. Unfortunately, the log-likelihood, although very simple, cannot be easily optimized because the normalization term requires summing over all the input values...