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
Export as PDF
Copy link