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Eclipse Deeplearning4j

Eclipse Deeplearning4j is 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 | Gitter chat | Documentation | Tutorials | API Reference | Sample Projects | FAQ | Stack Overflow Q&A

Platform

   

Social

     

System Requirements

#MinimumRecommended
14 GB RAM8 GB RAM

Ratings

Aggregate
3.83 
 5

G2CROWD3.5 
 5
based on 1 reviews
PAT RESEARCH8.2 
 10
based on 1 reviews
PAT RESEARCH7.8 
 10
based on professional's opinion

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

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