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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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Patrick R. Nicolas Patrick R. Nicolas
Author Profile Icon Patrick R. Nicolas
Patrick R. Nicolas
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Toc

Table of Contents (15) Chapters Close

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 A. Basic Concepts Index

An overview


The support for distributing and concurrent processing is provided by different stacked frameworks and libraries. Scala concurrent and parallel collections' classes leverage the threading capabilities of the Java virtual machine. Akka.io implements a reliable action model originally introduced as part of the Scala standard library. The Akka framework supports remote actors, routing, load balancing protocols, dispatchers, clusters, events, and configurable mailbox management. This framework also provides support for different transport modes, supervisory strategies, and typed actors. Apache Spark's resilient distributed datasets with advanced serialization, caching, and partitioning capabilities leverage Scala and Akka libraries.

The following stack representation illustrates the interdependencies between frameworks:

The Stack representation of scalable frameworks using Scala

Each layer adds a new functionality to the previous one to increase scalability. The Java virtual machine...

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