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 discussed the main principles of adversarial training and explained the roles of two players: the generator and discriminator. We described how to model and train them using a minimax approach whose double goal is to force the generator to learn the true data distribution pdata and get the discriminator to distinguish perfectly between true samples (belonging to pdata) and unacceptable ones. In the same section, we analyzed the inner dynamics of a GAN and some common problems that can slow down the training process and lead to a sub-optimal final configuration.

One of the most difficult problems experienced with standard GANs arises when the data-generating process and the generator distribution have disjointed support. In this case, the Jensen-Shannon divergence becomes constant and doesn't provide precise information about the distance. An excellent alternative is provided by the Wasserstein measure, which is employed in a more efficient model, called...