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

Adversarial training

The brilliant idea of adversarial training, proposed by Goodfellow et al. (in Goodfellow I. J., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Courville A., Bengio Y., Generative Adversarial Networks, arXiv:1406.2661 [stat.ML] – although this idea has been, at least in theory, discussed earlier by other authors), ushered in a new generation of generative models that immediately outperformed the majority of existing algorithms. All of the derived models are based on the same fundamental concept of adversarial training, which is an approach partially inspired by game theory.

Let's suppose that we have a data-generating process, , that represents an actual data distribution and a finite number of data points that we suppose are drawn from pdata:

Our goal is to train a model called a generator, whose distribution must be as close as possible to pdata. This is the trickiest part of the algorithm because instead of standard methods...