Deeplearning4j Roadmap

These priorities have been set by what the Skymind has seen demand for among clients and open-source community members. Contributors are welcome to add features whose priority they deem to be higher.

High priority:

  • CUDA rewrite for ND4J (under way)
  • CPU optimizations (C++ backend)
  • Hyperparameter optimization (underway, basics done: Arbiter)
  • Parameter server
  • Sparse support for ND4J
  • Performance tests for network training vs. other platforms (and where necessary: optimizations)
  • Performance tests for Spark vs. local (ditto)
  • Building examples at scale

Medium priority:

  • OpenCL for ND4J
  • CTC RNN (for speech etc.)

Nice to have:

  • Automatic differentiation
  • Proper complex number support for ND4J (+optimizations)
  • Reinforcement learning
  • Python support/interface
  • Support for ensembles
  • Variational autoencoders
  • Generative adversarial models

Low priority:

  • Hessian free optimization
  • Other RNN types: multi-dimensional; attention models, Neural Turing Machine, etc.
  • 3D CNNs

This is a work in progress. Last updated Feb. 27 2016.

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