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

Summary

In this chapter, we presented the natural evolution of TD(0) based on an average of backups with different lengths. The algorithm, called TD(), is extremely powerful, and it ensures faster convergence than TD(0), with only a few (non-restrictive) conditions. We also showed how to implement the Actor-Critic method with TD(0) in order to learn about both a stochastic policy and a value function.

In later sections, we discussed two methods based on the estimation of the Q function: SARSA and Q-learning. They are very similar, but the latter has a greedy approach, and its performance (in particular the training speed) results in it being superior to SARSA. The Q-learning algorithm is one of the most important models for the latest developments. In fact, it was the first RL approach employed with a deep convolutional network to solve complex environments (like Atari games). For this reason, we also presented a simple example based on an MLP that processes visual input and outputs...