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)
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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...