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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

Chapter 17. Apache Spark MLlib

The previous Chapter 16, Parallelism with Scala and Akka, provided the reader with different options to make computing-intensive applications scalable. These solutions are generic and do not address the specific needs of the data scientist. Optimization algorithms such as minimization of loss function or dynamic programming methods such as the Viterbi algorithm require support for caching data and broadcasting of model parameters. The Apache Spark framework addresses these shortcomings.

This chapter describes the key concepts behind the Apache Spark framework and its application to large scale machine learning problems. The reader is invited to dig into the wealth of books and papers on this topic. This last chapter describes four key characteristics of the Apache Spark framework:

MLlib functionality as illustrated with the K-means algorithm
Reusable ML pipelines, introduced in Spark 2.0
Extensibility of existing Spark functionality using Kullback-Leibler...

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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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