13 Facts About Deeplearning4j

Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine .

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.

Deeplearning4j was contributed to the Eclipse Foundation in October 2017.

Deeplearning4j relies on the widely used programming language Java, though it is compatible with Clojure and includes a Scala application programming interface .

Deeplearning4j has been used in several commercial and academic applications.

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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.

Deeplearning4j includes a vector space modeling and topic modeling toolkit, implemented in Java and integrating with parallel GPUs for performance.

Deeplearning4j includes implementations of term frequency–inverse document frequency, deep learning, and Mikolov's word2vec algorithm, doc2vec, and GloVe, reimplemented and optimized in Java.

Real-world use cases for Deeplearning4j include network intrusion detection and cybersecurity, fraud detection for the financial sector, anomaly detection in industries such as manufacturing, recommender systems in e-commerce and advertising, and image recognition.

Deeplearning4j has integrated with other machine-learning platforms such as RapidMiner, Prediction.

Deeplearning4j serves machine-learning models for inference in production using the free developer edition of SKIL, the Skymind Intelligence Layer.

Deeplearning4j is as fast as Caffe for non-trivial image recognition tasks using multiple GPUs.

Deeplearning4j can be used via multiple API languages including Java, Scala, Python, Clojure and Kotlin.