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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

Overview

Apache Spark is a fast and general-purpose cluster computing system, initially developed as AMPLab / UC Berkeley as part of the Berkeley Data Analytics Stack (BDAS), http://en.wikipedia.org/wiki/UC_Berkeley. It provides high-level APIs for the following programming languages that make large, concurrent parallel jobs easy to write and deploy [17:01]:

Scala: http://spark.apache.org/docs/latest/api/scala/index.html
Java:http://spark.apache.org/docs/latest/api/java/index.html
Python: http://spark.apache.org/docs/latest/api/python/index.html

Note

Link to latest information

The URLs as any reference to Apache Spark may change in future versions.

The core element of Spark is the resilient distributed dataset (RDD), which is a collection of elements partitioned across the nodes of a cluster and/or CPU cores of servers. An RDD can be created from a local data structure such as a list, array, or hash table, from the local filesystem or the Hadoop distributed file system (HDFS) [17:02].

The...

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