Book Image

Mastering Machine Learning Algorithms

Book Image

Mastering Machine Learning Algorithms

Overview of this book

Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn v0.19.1. You will also learn how to use Keras and TensorFlow 1.x to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need.
Table of Contents (22 chapters)
Title Page
Dedication
Packt Upsell
Contributors
Preface
13
Deep Belief Networks
Index

RBMs


A RBM (originally called Harmonium) is a neural model proposed by Smolensky (in Information processing in dynamical systems: Foundations of harmony theory, Smolensky P., Parallel Distributed Processing, Vol 1, The MIT Press) 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 Restricted Boltzmann Machine

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

A RBM is a probabilistic generative model that can learn a data-generating process, pdata, which is represented by the observed units but exploits the presence of the latent variables...