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

The back-propagation algorithm

This algorithm is more of a methodology than an actual algorithm. In fact, it was designed to be flexible enough to adapt to any kind of neural architecture without any substantial changes. Therefore, in this section we'll define the main concepts without focusing on a particular case. Those who are interested in implementing it will be able to apply the same techniques to different kinds of networks with minimal effort (assuming that all requirements are met).

The goal of a training process using a deep learning model is normally achieved by minimizing a cost function. Let's suppose we have a network parameterized with a global vector . In that case, the cost function (using the same notation for loss and cost but with different parameters to disambiguate) is defined as follows:

We've already explained that the minimization of the previous expression (which is the empirical risk) is a way to minimize the real expected risk and...