You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

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Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

5. Graph-Based Semi-Supervised Learning

6. Clustering and Unsupervised Models

7. Advanced Clustering and Unsupervised Models

8. Clustering and Unsupervised Models for Marketing

9. Generalized Linear Models and Regression

10. Introduction to Time-Series Analysis

11. Bayesian Networks and Hidden Markov Models

12. The EM Algorithm

13. Component Analysis and Dimensionality Reduction

14. Hebbian Learning

15. Fundamentals of Ensemble Learning

16. Advanced Boosting Algorithms

17. Modeling Neural Networks

18. Optimizing Neural Networks

19. Deep Convolutional Networks

20. Recurrent Neural Networks

21. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

26. Other Books You May Enjoy

27. 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)....