Eclipse Deeplearning4j

A distributed, deep learning library for Java virtual machine (JVM)

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Overview

Eclipse Deeplearning4j is a deep learning programming library written for Java and Scala and a computing framework with wide support for deep learning algorithms.

There are a lot of knobs to turn when you’re training a distributed deep-learning network. We’ve done our best to explain them, so that Eclipse Deeplearning4j can serve as a DIY tool for Java, Scala and Clojure programmers working on Hadoop and other file systems. - Official website

See Apache Hadoop.

Deeplearning4j includes implementations of the restricted Boltzmann machine, deep auto-encoder, deep belief net, stacked denoising auto-encoder and recursive neural tensor network, word2vec, doc2vec, and GloVe. These algorithms also include distributed parallel versions that integrate with Apache Hadoop and Apache Spark.

Release Notes I Gitter chat I Documentation I Tutorials I API Reference I Sample Projects I FAQ I Stack Overflow Q&A

Platform

  

Social

     

System Requirements

#MinimumRecommended
14 GB RAM8 GB RAM

Ratings

3.83
5
PAT RESEARCH: 8.2
10  based on 1 reviews

PAT RESEARCH: 7.8
10  based on professional's opinion

G2CROWD: 3.5
5  based on 1 reviews

Developer

Adam Gibson, Chris Nicholson, Josh Patterson, Others

Written in

Java, C++, Python, JavaScript, Scala, Cuda

Initial Release

22 February 2014

License

Apache v2


Notes

  • Please read the docs here completely for proper understanding of system requirements and configuration.
  • Eclipse Deeplearning4j will work on platform where you can have the prerequisites:
    • Java (developer version) 1.7 or later (Only 64-Bit versions supported)
    • Apache Maven (automated build and dependency manager)
    • IntelliJ IDEA or Eclipse
    • Git