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 started our exploration of the deep learning world by introducing the basic concepts that led the first researchers to improve algorithms until they achieved the top results we can achieve nowadays. The first part explained the structure of a basic artificial neuron, which combines a linear operation followed by an optional non-linear scalar function. A single layer of linear neurons was initially proposed as the first neural network, with the name of the perceptron.

Even though it was quite powerful for many problems, this model soon showed its limitations when working with non-linear separable datasets. A perceptron is not very different from logistic regression, and there's no concrete reason to employ it. Nevertheless, this model opened the doors to a family of extremely powerful models obtained by combining multiple non-linear layers. The multilayer perceptron, which has been proven to be a universal approximator, is able to manage almost any...