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  • 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
Last modified 1yr ago