IvyBazan commented on a change in pull request #15137: 1.5.0 news
URL: https://github.com/apache/incubator-mxnet/pull/15137#discussion_r296863167
 
 

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 File path: NEWS.md
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 @@ -17,6 +17,855 @@
 
 MXNet Change Log
 ================
+## 1.5.0
+
+### New Features
+
+#### Automatic Mixed Precision(experimental)
+Training Deep Learning networks is a very computationally intensive task. 
Novel model architectures tend to have increasing number of layers and 
parameters, which slows down training. Fortunately, new generations of training 
hardware as well as software optimizations, make it a feasible task. 
+However, where most of the (both hardware and software) optimization 
opportunities exists is in exploiting lower precision (like FP16) to, for 
example, utilize Tensor Cores available on new Volta and Turing GPUs. While 
training in FP16 showed great success in image classification tasks, other more 
complicated neural networks typically stayed in FP32 due to difficulties in 
applying the FP16 training guidelines.
+That is where AMP (Automatic Mixed Precision) comes into play. It 
automatically applies the guidelines of FP16 training, using FP16 precision 
where it provides the most benefit, while conservatively keeping in full FP32 
precision operations unsafe to do in FP16. To learn more about AMP, checkout 
this 
[tutorial](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/amp/amp_tutorial.md).
 
+
+#### MKL-DNN Reduced precision inference and RNN API support
+Two advanced features, fused computation and reduced-precision kernels, are 
introduced by MKL-DNN in the recent version. These features can significantly 
speed up the inference performance on CPU for a broad range of deep learning 
topologies. MXNet MKL-DNN backend provides optimized implementations for 
various operators covering a broad range of applications including image 
classification, object detection, natural language processing. Refer to the 
[MKL-DNN operator 
documentation](https://github.com/apache/incubator-mxnet/blob/v1.5.x/docs/tutorials/mkldnn/operator_list.md)
 for more information.
 
 Review comment:
   Consider changing to: "covering a broad range of applications including 
image classification, object detection, and natural language processing."

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