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Scala for Machine Learning, Second Edition

You're reading from   Scala for Machine Learning, Second Edition Build systems for data processing, machine learning, and deep learning

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787122383
Length 740 pages
Edition 2nd Edition
Languages
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Author (1):
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Patrick R. Nicolas Patrick R. Nicolas
Author Profile Icon Patrick R. Nicolas
Patrick R. Nicolas
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Table of Contents (21) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Data Pipelines 3. Data Preprocessing 4. Unsupervised Learning 5. Dimension Reduction 6. Naïve Bayes Classifiers 7. Sequential Data Models 8. Monte Carlo Inference 9. Regression and Regularization 10. Multilayer Perceptron 11. Deep Learning 12. Kernel Models and SVM 13. Evolutionary Computing 14. Multiarmed Bandits 15. Reinforcement Learning 16. Parallelism in Scala and Akka 17. Apache Spark MLlib A. Basic Concepts B. References Index

Chapter 15. Reinforcement Learning

You may wonder, at this stage of the book, how robotics, gaming, and autonomous systems leverage machine learning. The answer lies in a field of AI known as reinforcement learning. For those with no familiarity with reinforcement learning, I highly recommend you read the seminal book on reinforcement learning by R. Sutton and A. Barto [11:1] if you are interested to know about its origin, purpose, and scientific foundation.

The first part of this chapter focuses on the Q-learning algorithm. The second part is dedicated to Learning Classifier Systems (LCS), which combine reinforcement learning techniques with evolutionary computing, introduced in the previous chapter. Learning classifiers are an interesting breed of algorithm that is not commonly included in literature dedicated to machine learning.

In this chapter, you will learn the following:

  • Basic concepts behind reinforcement learning
  • Detailed implementation of the Q-learning algorithm
  • A simple...
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