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 analyzed the role of momentum and how it's possible to manage adaptive corrections using RMSProp. Then, we combined momentum and RMSProp to derive a very powerful algorithm called Adam. In order to provide a complete picture, we also presented two slightly different adaptive algorithms, called AdaGrad and AdaDelta.

In the next sections, we discussed regularization methods and how they can be plugged into a Keras model. An important section was dedicated to a very diffused technique called dropout, which consists of setting to zero (dropping) a fixed percentage of samples through random selection. This method, although very simple, prevents the overfitting of very deep networks and encourages the exploration of different regions of the sample space, obtaining a result not very dissimilar to the ones analyzed in Chapter 15, Fundamentals of Ensemble Learning. The last topic was the batch normalization technique, which is a method for reducing the...