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