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• Performance optimizations discussed above

• Added support for loading data in parallel with multiple processes to gluon.data.DataLoader. The number of workers can be set with num_worker. Does not support windows yet.

• Added Block.cast to support networks with different data types, e.g. float16.

• Added Lambda block for wrapping a user defined function as a block.

• Generalized `gluon.data.ArrayDataset` to support arbitrary number of arrays.

New Features - ARM / Raspberry Pi support [Experimental]

• MXNet now compiles and runs on ARMv6, ARMv7, ARMv64 including Raspberry Pi devices. See https://github.com/apache/incubator-mxnet/tree/master/docker_multiarch for more information.

New Features - NVIDIA Jetson support [Experimental]

• MXNet now compiles and runs on NVIDIA Jetson TX2 boards with GPU acceleration.

• You can install the python MXNet package on a Jetson board by running - `$ pip install mxnet-jetson-tx2`

New Features - Sparse Tensor Support [General Availability]

• Added more sparse operators: `contrib.SparseEmbedding`, `sparse.sum` and `sparse.mean`

• Added `asscipy()` for easier conversion to scipy

• Added `check_format()` for sparse ndarrays to check if the array format is valid

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