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Scala for Machine Learning, Second Edition - Second Edition

You're reading from Scala for Machine Learning, Second Edition - Second Edition

Product type Book

Published in Sep 2017

Publisher Packt

ISBN-13 9781787122383

Pages 740 pages

Edition 2nd Edition

Languages

Concepts

Machine Learning

Table of Contents (27) Chapters

Scala for Machine Learning Second Edition

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. Getting Started

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

Basic Concepts

References

Index

Scalability with Actors

Traditional multithreaded applications rely on accessing data located in shared memory. The mechanism relies on synchronization monitors such as locks, mutexes, or semaphores to avoid deadlocks and inconsistent mutable states. Even for the most experienced software engineer, debugging multithreaded applications is not a simple endeavor.

The second problem with shared memory threads in Java is the high computation overhead caused by continuous context switches. Context switching consists of saving the current stack frame delimited by the base and stack pointers into the heap memory and loading another stack frame.

These restrictions and complexities can be avoided by using a concurrency model that relies on the following key principles:

Immutable data structures
Asynchronous communication

The Actor model

The Actor model, originally introduced in the Erlang programming language, addresses these issues [16:3]. The purpose of using the Actor model is two-fold:

It distributes...

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