- 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-8.0backend 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