0.6.0

• 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