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Machine Learning: End-to-End guide for Java developers

You're reading from   Machine Learning: End-to-End guide for Java developers Data Analysis, Machine Learning, and Neural Networks simplified

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Product type Course
Published in Oct 2017
Publisher Packt
ISBN-13 9781788622219
Length 1159 pages
Edition 1st Edition
Languages
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Authors (2):
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Krishna Choppella Krishna Choppella
Author Profile Icon Krishna Choppella
Krishna Choppella
Uday Kamath Uday Kamath
Author Profile Icon Uday Kamath
Uday Kamath
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Chapter 2. Java Libraries and Platforms for Machine Learning

Implementing machine learning algorithms by yourself is probably the best way to learn machine learning, but you can progress much faster if you step on the shoulders of the giants and leverage one of the existing open source libraries.

This chapter reviews various libraries and platforms for machine learning in Java. The goal is to understand what each library brings to the table and what kind of problems is it able to solve?

In this chapter, we will cover the following topics:

  • The requirement of Java to implement a machine learning app
  • Weka, a general purpose machine learning platform
  • Java machine learning library, a collection of machine learning algorithms
  • Apache Mahout, a scalable machine learning platform
  • Apache Spark, a distributed machine learning library
  • Deeplearning4j, a deep learning library
  • MALLET, a text mining library

We'll also discuss how to architect the complete machine learning app stack for both single...

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