Deeplearning4j

Deeplearning4j
Original author(s)	Adam Gibson
Developer(s)	various

Stable release	0.0.4 / 24 August 2015 (2015-08-24)
Development status	Active
Written in	Java, Scala, CUDA, C, C++
Operating system	Linux, OS X, Windows, Android
Platform	Cross-platform
Available in	English
Type	Natural language processing, deep learning, machine vision, artificial intelligence
License	Apache 2.0
Website	deeplearning4j.org

Machine learning and data mining

Problems Classification Clustering Regression Anomaly detection Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction
Supervised learning (classification • regression) Decision trees Ensembles (Bagging, Boosting, Random forest) k-NN Linear regression Naive Bayes Neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM)
Clustering BIRCH Hierarchical k-means Expectation-maximization (EM) DBSCAN OPTICS Mean-shift
Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA t-SNE
Structured prediction Graphical models (Bayes net, CRF, HMM)
Anomaly detection k-NN Local outlier factor
Neural nets Autoencoder Deep learning Multilayer perceptron RNN Restricted Boltzmann machine SOM Convolutional neural network
Reinforcement Learning Q-Learning SARSA Temporal Difference (TD)
Theory Bias-variance dilemma Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical learning VC theory
Machine learning venues NIPS ICML IJMLC ML JMLR ArXiv:cs.LG
Machine learning portal

Deeplearning4j is a deep learning programming library written for Java and the Java virtual machine (JVM)^[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, word2vec, doc2vec, and GloVe. These algorithms all include distributed parallel versions that integrate with Apache Hadoop and Spark.^[4]

Deeplearning4j is open-source software released under Apache License 2.0,^[5] developed mainly by a machine learning group in San Francisco led by Adam Gibson.^[6]^[7] It is supported commercially by the startup Skymind.

Introduction

Deeplearning4j relies on the widely used programming language, Java, though it is compatible with Clojure and includes a Scala application programming interface (API). It is powered by its own open-source numerical computing library, ND4J, and works with both central processing units (CPUs) and graphics processing units (GPUs).^[8]^[9]

Deeplearning4j has been used in several commercial and academic applications. The code is hosted on GitHub.^[10] A support forum is maintained on Gitter.^[11]

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. It also has extensive visualization tools,^[12] and a computation graph.^[13]

Distributed

Training with Deeplearning4j occurs in a cluster. Neural nets are trained in parallel via iterative reduce, which works on Hadoop-YARN and on Spark.^[6]^[14] 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 scientific computing in Java and Scala, similar to the functions that NumPy provides to Python. It's effectively based on a library for linear algebra and matrix manipulation in a production environment.

DataVec vectorization library for machine-learning

DataVec vectorizes various file formats and data types using an input/output format system similar to Hadoop's use of MapReduce; that is, it turns various data types into columns of scalars termed vectors. DataVec is designed to vectorize CSVs, images, sound, text, video, and time series.

Text and NLP

Deeplearning4j includes a vector space modeling and topic modeling toolkit, implemented in Java and integrating with parallel GPUs for performance. It is designed to handle large text sets.

Deeplearning4j includes implementations of term frequency–inverse document frequency (tf–idf), deep learning, and Mikolov's word2vec algorithm,^[15] doc2vec, and GloVe, reimplemented and optimized in Java. It relies on t-distributed stochastic neighbor embedding (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,^[16] anomaly detection in industries such as manufacturing, recommender systems in e-commerce and advertising,^[17] and image recognition. Deeplearning4j has integrated with other machine-learning platforms such as RapidMiner and Prediction.io.^[18]

Related libraries

OpenNN, an open source neural networks library written in C++ for deep learning.
Torch, an open source framework written in Lua with wide support for machine learning algorithms.
Theano, an open source deep learning library for Python.
Neuroph

References

↑ Metz, Cade (2014-06-02). "The Mission to Bring Google's AI to the Rest of the World". Wired.com. Retrieved 2014-06-28.
↑ Vance, Ashlee (2014-06-03). "Deep Learning for (Some of) the People". Bloomberg Businessweek. Retrieved 2014-06-28.
↑ Novet, Jordan (2015-11-14). "Want an open-source deep learning framework? Take your pick". VentureBeat. Retrieved 2015-11-24.
↑ 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.
↑ "Github Repository".
1 2 "deeplearning4j.org".
↑ "Crunchbase Profile".
↑ Harris, Derrick (2014-06-02). "A startup called Skymind launches, pushing open source deep learning". GigaOM.com. Retrieved 2014-06-29.
↑ Novet, Jordan (2014-06-02). "Skymind launches with open-source, plug-and-play deep learning features for your app". Retrieved 2014-06-29.
↑ Deeplearning4j source code
↑ Deeplearning4j Gitter Support Channel
↑ Deeplearning4j Visualization Tools
↑ Deeplearning4j Deeplearning4j Computation Graph
↑ "Iterative reduce".
↑ word2vec
↑ http://www.skymind.io/finance/
↑ http://www.skymind.io/commerce/
↑ https://www.rapidminerchina.com/en/products/shop/product/deeplearning4j/

External links

Computer vision

Categories	Datasets Digital geometry Commercial systems Feature detection Geometry Image sensor technology Learning Morphology Motion analysis Noise reduction techniques Recognition and categorization Research infrastructure Researchers Segmentation Software

Technologies	Computer stereo vision

Applications	Autonomous vehicles Face recognition Image search Optical character recognition Remote sensing Robots

Main category

Deep learning software

Open-source	Apache Singa Deeplearning4j OpenNN Keras Microsoft Cognitive Toolkit TensorFlow Theano Torch

Proprietary	Neural Designer Wolfram Mathematica

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