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

View More author details

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

Deep Belief Networks

In this chapter, we're going to present two probabilistic generative models that employ a set of latent variables to represent a specific data generation process. Restricted Boltzmann Machines (RBMs), proposed in 1986, are the building blocks of a more complex model, called a Deep Belief Network (DBN), which is capable of capturing complex relationships among features at different levels (in a way not dissimilar to a deep convolutional network). Both models can be used in unsupervised and supervised scenarios as preprocessors or, as is usual with DBN, fine-tuning the parameters using a standard backpropagation algorithm.

In particular, we will discuss:

Markov random fields (MRF)
RBM, including the Contrastive Divergence (CD-k) algorithm
DBN with supervised and unsupervised examples

We can now discuss the fundamental theoretical concept behind this model family: the Markov random fields, showing their properties and how they can be...