...

• 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.

• 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.

• Better support for IDE auto-completion. IDEs like PyCharm can now correctly parse mxnet operators.
  

New Features - Sparse Tensor Support

• Added support for sparse matrices. See documentation for more info.
  
• Added limited cpu support for two sparse formats in Symbol and NDArray - CSRNDArray and RowSparseNDArray.
• Added a sparse dot product operator and many element-wise sparse operators.
• Added a data iterator for sparse data input - LibSVMIter.
• Added three optimizers for sparse gradient updates: Ftrl, SGD and Adam.
• Added push and row_sparse_pull with RowSparseNDArray in distributed kvstore.Better support for IDE auto-completion. IDEs like PyCharm can now correctly parse mxnet operators.

API Changes

• Operators like mx.sym.linalg_* and mx.sym.random_* are now moved to mx.sym.linalg.* and mx.sym.random.*. The old names are still available but deprecated.

• sample_* and random_* are now merged as random.*, which supports both scalar and  NDArray distribution parameters.

...