Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
26
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27
Index

Restricted Boltzmann Machines

A Restricted Boltzmann Machine (RBM), originally called a Harmonium, is a neural model proposed by Smolensky (in Smolensky P., Information processing in dynamical systems: Foundations of harmony theory, Parallel Distributed Processing, Vol 1, The MIT Press, 1986) that is made up of a layer of input (observable) neurons and a layer of hidden (latent) neurons. A generic structure is shown in the following diagram:

Structure of an RBM

As the undirected graph is bipartite (there are no connections between neurons belonging to the same layer), the underlying probabilistic structure is an MRF. In the original model (even if this is not a restriction), all the neurons are assumed to be Bernoulli-distributed (xi,hj = {0,1}), with a bias bi (for the observed units) and cj (for the latent neurons). The resulting energy function is:

An RBM is a probabilistic generative model that can learn a data-generating process, pdata, which is...