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

EM Algorithm

The EM algorithm is a generic framework that can be employed in the optimization of many generative models. It was originally proposed in Dempster A. P., Laird N. M., Rubin D. B., Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, B, 39(1):1–38, 11/1977, where the authors also proved its convergence at different levels of genericity. Many machine learning problems have the objective of finding a flexible way to express the data-generating process behind datasets. For example, given a set of pictures representing faces , we are generally interested in discovering at least an approximation of the distribution pdata from where the training sample has been drawn.

The reason is obvious: we can never work with all possible data points and, moreover, a synthetic expression (for example, a neural network or a mixture of distributions) allows us to draw new samples or to evaluate the likelihood of other datasets.

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