Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs. Skymind is its commercial support arm.
DL4J takes advantage of the latest distributed computing frameworks including Hadoop and Apache Spark to improve training.
Completely open-source and is maintained by the community and its commercial arm, Skymind.
Deeplearning4j is written in Java and is compatible with any JVM language. Import into Clojure, Scala, & Kotlin projects.
"I'm very keen on using Deeplearning4j in production here. This is a massive opportunity in a market worth billions of quid." - John M.
Deeplearning4j aims to be cutting-edge plug and play, more convention than configuration, which allows for fast prototyping for non-researchers. DL4J is customizable at scale. Released under the Apache 2.0 license, all derivatives of DL4J belong to their authors.
If you have any questions, please join us on Gitter; for premium support, contact us at Skymind. ND4J is the Java-based scientific computing engine powering our matrix operations. On large matrices, our benchmarks show it runs roughly twice as fast as Numpy.
A. DL4J is applied for Fraud detection, Recommender Systems (CRM, adtech, churn prevention), Regression and predictive analytics, Face/image recognition, Voice search, Speech-to-text (transcription), and preventative hardware monitoring (anomaly detection).
A. With a versatile n-dimensional array class for Java and Scala, DL4J is Scalable on Hadoop, utlizes GPU support for scaling on AWS, includes a general vectorization tool for machine-learning libs, and most of all relies on ND4J: A matrix library much faster than Numpy and largely written in C++.
A. Developers who would like to contribute to Deeplearning4j can get started by reading our Developer's Guide.
A. Deeplearning4j includes both a distributed, multi-threaded deep-learning framework and a normal single-threaded deep-learning framework. Training takes place in the cluster, which means it can process massive amounts of data quickly. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure Deeplearning4j's role as a modular component in an open stack makes it the first deep-learning framework adapted for a micro-service architecture.
Deep neural nets are capable of record-breaking accuracy. For a quick neural net introduction, please visit our overview page. In a nutshell, Deeplearning4j lets you compose deep neural nets from various shallow nets, each of which form a so-called `layer`. This flexibility lets you combine restricted Boltzmann machines, other autoencoders, convolutional nets or recurrent nets as needed in a distributed, production-grade framework that works with Spark and Hadoop on top of distributed CPUs or GPUs.
There are a lot of parameters to adjust when you're training a deep-learning network. We've done our best to explain them, so that Deeplearning4j can serve as a DIY tool for Java, Scala and Clojure programmers.