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Gamma and Poisson distributions (#27)
* Added implementation for random_gamma op.

* Added implementation for random_poisson op and support classes.

* Added helpers for random_poisson and random_gamma ops.

* Implementation of random_poisson. The first working edition.

* Implementation of random_poisson. Parallelized working edition.

* Implementation of random_gamma. Parallelized working edition with alpha only.

* Added cuda implementation for helper of poisson distribution.

* Corrected shape calculation with random_gamma and tests.

* Finished cpu implementation for gamma distribution.

* Finished cuda implementation for random_gamma op.

* Refactored cpu helpers for random_gamma and random_poisson ops.

* Refactored cuda helpers for gamma and poisson distribution.

* Refactored cuda helper for gamma distribution.

* Refactored cpu helper for random_poisson op.

* Refactored cpu helper for random_gamma op.
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README.md

Monorepo of Deeplearning4j

Welcome to the new monorepo of Deeplearning4j that contains the source code for all the following projects, in addition to the original repository of Deeplearning4j moved to deeplearning4j:

To build everything, we can use commands like

./change-cuda-versions.sh x.x
./change-scala-versions.sh 2.xx
./change-spark-versions.sh x
mvn clean install -Dmaven.test.skip -Dlibnd4j.cuda=x.x -Dlibnd4j.compute=xx

or

mvn -B -V -U clean install -pl '!jumpy,!pydatavec,!pydl4j' -Dlibnd4j.platform=linux-x86_64 -Dlibnd4j.chip=cuda -Dlibnd4j.cuda=9.2 -Dlibnd4j.compute=<your GPU CC> -Djavacpp.platform=linux-x86_64 -Dmaven.test.skip=true

An example of GPU "CC" or compute capability is 61 for Titan X Pascal.

Want some examples?

We have separate repository with various examples available: https://github.com/eclipse/deeplearning4j-examples

In the examples repo, you'll also find a tutorial series in Zeppelin: https://github.com/eclipse/deeplearning4j-examples/tree/master/tutorials