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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 16. Parallelism in Scala and Akka

Data analysts, scientists, and software engineers have been facing a serious challenge: the explosion of the amount of data required to build reliable models. After all, how valuable is a data mining application if the model does not scale?

The challenge of big data is addressed through a two-facet strategy: improving the efficiency of existing data mining and machine learning solutions, and leveraging scalable infrastructure (frameworks, programming languages, GPUs, and so on).

This chapter covers the Scala parallel collections, the Actor model, and the Akka framework. The next chapter introduces the Apache Spark framework and its collection of machine learning algorithms.

The following are the topics addressed in this chapter:

Introduction to Scala parallel collections
Evaluation of the performance of a parallel collection on a multicore CPU
The Actor model and reactive systems
Clustered and reliable distributed computing using Akka
Design of...

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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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