It's effectively based on a library for linear algebra and matrix manipulation in a production environment.ĭataVec vectorization library for machine-learning ĭataVec 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. Scientific computing for the JVM ĭeeplearning4j 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 also integrates with CUDA kernels to conduct pure GPU operations, and works with distributed GPUs. Neural nets are trained in parallel via iterative reduce, which works on Hadoop-YARN and on Spark. Training with Deeplearning4j occurs in a cluster.

It also has extensive visualization tools, and a computation graph. 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.

ĭeeplearning4j has been used in several commercial and academic applications. 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). Introduction ĭeeplearning4j relies on the widely used programming language Java, though it is compatible with Clojure and includes a Scala application programming interface (API). Deeplearning4j was contributed to the Eclipse Foundation in October 2017. It is supported commercially by the startup Skymind, which bundles DL4J, TensorFlow, Keras and other deep learning libraries in an enterprise distribution called the Skymind Intelligence Layer. ĭeeplearning4j is open-source software released under Apache License 2.0, developed mainly by a machine learning group headquartered in San Francisco. These algorithms all include distributed parallel versions that integrate with Apache Hadoop and Spark. 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.

It is a framework with wide support for deep learning algorithms. Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine (JVM).

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