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ND4J is an Apache 2.0-licensed scientific computing library for the JVM. By contributing code to this repository, you agree to make your contribution available under an Apache 2.0 license.
It is meant to be used in production environments rather than as a research tool, which means routines are designed to run fast with minimum RAM requirements.
Please search for the latest version on search.maven.org.
- Supports GPUs via with the CUDA backend nd4j-cuda-7.5 and Native via nd4j-native.
- All of this is wrapped in a unifying interface.
- The API mimics the semantics of Numpy, Matlab and scikit-learn.
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## Documentation
Documentation is available at [deeplearning4j.org](https://deeplearning4j.org/). Access the [JavaDocs](https://deeplearning4j.org/api/latest/) for more detail.
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## Installation
To install ND4J, there are a couple of approaches, and more information can be found on the [DL4J website](https://deeplearning4j.org/docs/latest/nd4j-overview).
3. Fork [the repository](https://github.com/eclipse/deeplearning4j.git) on GitHub to start making your changes to the **master** branch (or branch off of it).