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

Deep Belief Networks

A belief or Bayesian network is a concept already explored in Chapter 11, Bayesian Networks and Hidden Markov Models. In this particular case, we are going to consider Belief networks where there are visible and latent variables, organized into homogeneous layers. The first layer always contains the input (visible) units, while all the remaining ones are latent. Hence, a DBN can be structured as a stack of RBMs, where each hidden layer is also the visible one of the subsequent RBM, as shown in the following diagram (the number of units can be different for each layer):

Structure of a generic DBN

The learning procedure is usually greedy and stepwise (as proposed in Hinton G. E., Osindero S., Teh Y. W., A fast learning algorithm for deep belief nets, Neural Computation, 18/7, 2006). The first RBM is trained with the dataset and optimized to reconstruct the original distribution using the CD-k algorithm. At this point, the internal (hidden...