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

Fundamental concepts of RL

Imagine that you want to learn to ride a bike, and ask a friend for advice. They explain how the gears work, how to release the brake and a few other technical details. In the end, you ask the secret to keeping your balance.

What kind of answer do you expect? In an imaginary supervised world, you should be able to perfectly quantify your actions and correct errors by comparing the outcomes with precise reference values. In the real world, you have no idea about the quantities underlying your actions and, above all, you will never know what the right value is.

Increasing the level of abstraction, the scenario we're considering can be described as: a generic agent performs actions inside an environment and receives feedback that is somehow proportional to the competence of its actions. According to this Feedback, the Agent can correct its actions in order to reach a specific goal. This basic schema is represented in the following diagram:

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