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
Other Books You May Enjoy
27
Index

Introduction to Generative Adversarial Networks

In this chapter, we're going to provide a brief introduction to a family of generative models based on some game theory concepts. Their main peculiarity is an adversarial training procedure that is aimed at learning to distinguish between true and fake samples, driving, at the same time, another component that generates samples more and more similar to the training examples.

In particular, we will be discussing:

  • Adversarial training and standard Generative Adversarial Networks (GANs)
  • Deep Convolutional GANs (DCGANs)
  • Wasserstein GANs (WGANs)

We can now introduce the concept of adversarial training of neural models, its connection to game theory and its applications to GANs.