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• Added enhancements to the Gluon package, a high-level interface designed to be easy to use while keeping most of the flexibility of low level API. Gluon supports both imperative and symbolic programming, making it easy to train complex models imperatively with minimal impact on performance. Neural networks (and other machine learning models) can be defined and trained with gluon.nn and gluon.rnn packages. 

• Added new loss functions - SigmoidBinaryCrossEntropyLoss, CTCLoss, HuberLoss, HingeLoss, SquaredHingeLoss, LogisticLoss, TripletLoss.

• gluon.Trainer now allows reading and setting learning rate with trainer.learning_rate property.

• Added API HybridBlock.export for exporting gluon models to MXNet format.

• Added ConvLSTM to gluon.contrib.

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• Added limited support for fancy indexing, which allows you to very quickly access and modify complicated subsets of an array's values. x[idx_arr0, idx_arr1, ..., idx_arrn] is now supported. Full support coming soon in next release. Checkout master to get a preview.
  Added new loss functions - SigmoidBinaryCrossEntropyLoss, CTCLoss, HuberLoss, HingeLoss, SquaredHingeLoss, LogisticLoss, TripletLoss.

• Random number generators in mx.nd.random.* and mx.sym.random.* now support both CPU and GPU.

• NDArray and Symbol now supports "fluent" methods. You can now use x.exp() etc instead of mx.nd.exp(x) or mx.sym.exp(x).

• Added mx.rtc.CudaModule for writing and running CUDA kernels from python. See documentation for examples. 

• Added multi_precision option to optimizer for easier float16 training.

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