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Scala for Machine Learning

You're reading from   Scala for Machine Learning Leverage Scala and Machine Learning to construct and study systems that can learn from data

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783558742
Length 624 pages
Edition 1st 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 (15) Chapters Close

Preface 1. Getting Started 2. Hello World! FREE CHAPTER 3. Data Preprocessing 4. Unsupervised Learning 5. Naïve Bayes Classifiers 6. Regression and Regularization 7. Sequential Data Models 8. Kernel Models and Support Vector Machines 9. Artificial Neural Networks 10. Genetic Algorithms 11. Reinforcement Learning 12. Scalable Frameworks A. Basic Concepts Index

Appendix A. Basic Concepts

Machine learning algorithms make significant use of linear algebra and optimization techniques. Describing the concepts and the implementation of linear algebra, calculus, and optimization algorithms in detail would have added significant complexity to the book and distracted the reader from the essence of machine learning.

This appendix lists a set of basic elements of linear algebra and optimization mentioned throughout the book. It also summarizes the coding practices that have been covered, and acquaints the reader with basic knowledge of financial analysis.

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