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

View More author details

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

Multilayer Perceptrons (MLPs)

The main limitation of a perceptron is its linearity. How is it possible to exploit this kind of architecture by removing such a constraint? The solution is easier than you might speculate. Adding at least one non-linear layer between the input and output leads to a highly non-linear combination, parametrized with a larger number of variables. The resulting architecture, called a Multilayer Perceptron (MLP) and containing a single (just for simplicity) hidden layer, is shown in the following diagram:

Structure of a generic Multilayer Perceptron with a single hidden layer

This is a so-called feed-forward network, meaning that the flow of information begins in the first layer, always proceeds in the same direction, and ends at the output layer. Architectures that allow partial feedback (for example, in order to implement local memory) are called recurrent networks and will be analyzed in the next chapter.

In this case, there...