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Deeplearning4j

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Deeplearning4j
Original author(s)Adam Gibson
Developer(s)various
Stable release
0.0.4 / 24 August 2015; 9 years ago (2015-08-24)
Repository
Written inJava, Scala, CUDA, C
Operating systemLinux, OSX, Windows, Android, CyanogenMod
PlatformCross-platform
TypeNatural language processing, Deep learning, Machine vision, Artificial intelligence
LicenseApache 2.0
Websitedeeplearning4j.org
Part of a series on
Machine learning
and data mining
Paradigms
Problems
Clustering
Dimensionality reduction
Structured prediction
Anomaly detection
Artificial neural network
Reinforcement learning
Learning with humans
Model diagnostics
Mathematical foundations
Journals and conferences
Related articles

Deeplearning4j is an open source deep learning library written for Java and the Java Virtual Machine[1][2] and a computing framework with wide support for deep learning algorithms.[3] Deeplearning4j includes implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoencoder and recursive neural tensor network, as well as word2vec, doc2vec and GloVe. These algorithms all include distributed parallel versions that integrate with Hadoop and Spark.[4] It is commercially supported by the startup Skymind.

Introduction

Deeplearning4j relies on the widely used programming language, Java - though it is compatible with Clojure and includes a Scala API. It is powered by its own open-source numerical computing library, ND4J, and works with both CPUs and GPUs.[5] [6] Deeplearning4j is an open source project[7] primarily developed by a machine learning group in San Francisco led by Adam Gibson.[8][9] Deeplearning4j is the only open-source project listed on Google's Word2vec page for its Java implementation.[10]

Deeplearning4j has been used in a number of commercial and academic applications. The code is hosted on GitHub[11] and a support forum is maintained on Google Groups.[12]

The framework is composable, meaning shallow neural nets such as restricted Boltzmann machines, convolutional nets, autoencoders and recurrent nets can be added to one another to create deep nets of varying types.

Distributed

Training with Deeplearning4j takes place in a cluster. Neural nets are trained in parallel via iterative reduce, which works on Hadoop/YARN and on Spark.[8][13] Deeplearning4j also integrates with Cuda kernels to conduct pure GPU operations, and works with distributed GPUs.

Scientific Computing for the JVM

Deeplearning4j includes an n-dimensional array class using ND4J that allows for scientific computing in Java and Scala, similar to the functionality that Numpy provides to Python. It's effectively based on a library for linear algebra and matrix manipulation in a production environment.

Canova Vectorization Lib for Machine-Learning

Canova vectorizes[clarification needed] various file formats and data types using an input/output format system similar to Hadoop's use of MapReduce. A work in progress, Canova is designed to vectorize CSVs, images, sound, text and video. Canova can be used from the command line.

Text & NLP

Deeplearning4j includes a vector space modeling and topic modeling toolkit, implemented in Java and integrating with parallel GPUs for performance. It is specifically intended for handling large text collections.

Deeplearning4j includes implementations of tf–idf, deep learning, and Mikolov's word2vec algorithm, doc2vec and GloVe - reimplemented and optimized in Java. It relies on t-SNE for word-cloud visualizations.

Real-World Use Cases and Integrations

Real-world use cases for Deeplearning4j include fraud detection for the financial sector,[14] anomaly detection in industries such as manufacturing, recommender systems in e-commerce and advertising,[15] and image recognition. Deeplearning4j has integrated with other machine-learning platforms such as RapidMiner and Prediction.io. [16]

See also

References

  1. ^ Metz, Cade (2014-06-02). "The Mission to Bring Google's AI to the Rest of the World". Wired.com. Retrieved 2014-06-28.
  2. ^ Vance, Ashlee (2014-06-03). "Deep Learning for (Some of) the People". Bloomberg Businessweek. Retrieved 2014-06-28.
  3. ^ Novet, Jordan (2015-11-14). "Want an open-source deep learning framework? Take your pick". VentureBeat. Retrieved 2015-11-24.
  4. ^ TV, Functional (2015-02-12). "Adam Gibson, DeepLearning4j on Spark and Data Science on JVM with nd4j, SF Spark @Galvanize 20150212". SF Spark Meetup. Retrieved 2015-03-01.
  5. ^ Harris, Derrick (2014-06-02). "A startup called Skymind launches, pushing open source deep learning". GigaOM.com. Retrieved 2014-06-29.
  6. ^ Novet, Jordan (2014-06-02). "Skymind launches with open-source, plug-and-play deep learning features for your app". Retrieved 2014-06-29.
  7. ^ "Github Repository".
  8. ^ a b "deeplearning4j.org".
  9. ^ "Crunchbase Profile".
  10. ^ "Google Code".
  11. ^ Deeplearning4j source code
  12. ^ Deeplearning4j Google Group
  13. ^ "Iterative reduce".
  14. ^ http://www.skymind.io/finance/
  15. ^ http://www.skymind.io/commerce/
  16. ^ https://www.rapidminerchina.com/en/products/shop/product/deeplearning4j/
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