• Custom layer support

  • Support for custom loss functions

  • Support for compressed INDArrays, for memory saving on huge data

  • Native support for BooleanIndexing where applicable

  • Initial support for combined operations on CUDA

  • Significant performance improvements on CPU & CUDA backends

  • Better support for Spark environments using CUDA & cuDNN with multi-gpu clusters

  • New UI tools: FlowIterationListener and ConvolutionIterationListener, for better insights of processes within NN.

  • Special IterationListener implementation for performance tracking: PerformanceListener

  • Inference implementation added for ParagraphVectors, together with option to use existing Word2Vec model

  • Severely decreased file size on the deeplearnning4j api

  • nd4j-cuda-8.0 backend is available now for cuda 8 RC

  • Added multiple new built-in loss functions

  • Custom preprocessor support

  • Performance improvements to Spark training implementation

  • Improved network configuration validation using InputType functionality

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