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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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...