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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Index

Wasserstein GAN

As explained in the previous section, one of the most difficult problems with standard GANs is caused by the loss function based on the Jensen-Shannon divergence, whose value becomes constant when two distributions have disjointed supports. This situation is quite common with high-dimensional, semantically structured datasets. For example, images are constrained to having particular features in order to represent a specific subject (this is a consequence of the manifold assumption discussed in Chapter 3, Introduction to Semi-Supervised Learning). The initial generator distribution is very unlikely to overlap a true dataset, and in many cases, they are also very far from each other. This condition increases the risk of learning the wrong representation (a problem known as mode collapse), even when the discriminator is able to distinguish between true and generated samples (such a condition arises when the discriminator learns too quickly with respect to the generator)....