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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

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781787122383

Length 740 pages

Edition 2nd Edition

Languages

Scala

Tools

Apache Spark

Concepts

Data Processing

Author (1):

Patrick R. Nicolas

View More author details

Table of Contents (21) Chapters

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

Leveraging Java libraries

There are numerous robust, accurate, and efficient Java libraries for mathematics, linear algebra, or optimization that have been widely used for many years:

JBlas/Linpack: https://github.com/mikiobraun/jblas
Parallel Colt: https://github.com/rwl/ParallelColt
Apache Commons Math: http://commons.apache.org/proper/commons-math

There is absolutely no need to rewrite, debug, and test these components in Scala. Developers should consider creating a wrapper or interface to his/her favorite and reliable Java library. The book leverages the Apache Commons Math library for some specific linear algebra algorithms.

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Scala for Machine Learning, Second Edition - Second Edition

Published in: Sep 2017

Publisher: Packt

ISBN-13: 9781787122383

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Authors (1)

R. Nicolas

R. Nicolas

Patrick R. Nicolas is the director of engineering at Agile SDE, California. He has more than 25 years of experience in software engineering and building applications in C++, Java, and more recently in Scala/Spark, and has held several managerial positions. His interests include real-time analytics, modeling, and the development of nonlinear models.

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