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Scala for Machine Learning

You're reading from   Scala for Machine Learning Leverage Scala and Machine Learning to construct and study systems that can learn from data

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783558742
Length 624 pages
Edition 1st Edition
Languages
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Author (1):
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 R. Nicolas R. Nicolas
Author Profile Icon R. Nicolas
R. Nicolas
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Table of Contents (20) Chapters Close

Scala for Machine Learning
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started FREE CHAPTER 2. Hello World! 3. Data Preprocessing 4. Unsupervised Learning 5. Naïve Bayes Classifiers 6. Regression and Regularization 7. Sequential Data Models 8. Kernel Models and Support Vector Machines 9. Artificial Neural Networks 10. Genetic Algorithms 11. Reinforcement Learning 12. Scalable Frameworks Basic Concepts Index

Summary


This completes the introduction of the most common scalable frameworks built using Scala. It is quite challenging to describe frameworks, such as Akka and Spark, as well as new computing models such as Actors, futures, and RDDs, in a few pages. This chapter should be regarded as an invitation to further explore the capabilities of those frameworks in both a single host and a large deployment environment.

In this last chapter, we learned:

  • The benefits of asynchronous concurrency

  • The essentials of the actor model and composing futures with blocking or callback modes

  • How to implement a simple Akka cluster to squeeze performance of distributed applications

  • The ease and blazing performance of Spark's resilient distributed datasets and the in-memory persistency approach

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