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Mastering Apache Spark 2.x - Second Edition

You're reading from Mastering Apache Spark 2.x - Second Edition

Product type Book

Published in Jul 2017

Publisher Packt

ISBN-13 9781786462749

Pages 354 pages

Edition 2nd Edition

Languages

Concepts

Table of Contents (21) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. A First Taste and What’s New in Apache Spark V2

2. Apache Spark SQL

3. The Catalyst Optimizer

4. Project Tungsten

5. Apache Spark Streaming

6. Structured Streaming

7. Apache Spark MLlib

8. Apache SparkML

9. Apache SystemML

10. Deep Learning on Apache Spark with DeepLearning4j and H2O

11. Apache Spark GraphX

12. Apache Spark GraphFrames

13. Apache Spark with Jupyter Notebooks on IBM DataScience Experience

14. Apache Spark on Kubernetes

Summary

This chapter has attempted to provide you with an overview of some of the functionality available within the Apache Spark MLlib module. It has also shown the functionality that will soon be available in terms of ANNs or artificial neural networks. You might have been impressed how well ANNs work, so there is a lot more on ANNs in a later Chapter covering DeepLearning. It is not possible to cover all the areas of MLlib due to the time and space allowed for this chapter. In addition, we now want to concentrate more on the SparkML library in the next chapter, which speeds up machine learning by supporting DataFrames and the underlying Catalyst and Tungsten optimizations.

We saw how to develop Scala-based examples for Naive Bayes classification, K-Means clustering, and ANNs. You learned how to prepare test data for these Spark MLlib routines. You also saw that they all accept the LabeledPoint structure, which contains features and labels.

Additionally, each approach takes a training and...

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