eric-haibin-lin commented on a change in pull request #10391: [MXNET-139] Tutorial for mixed precision training with float16 URL: https://github.com/apache/incubator-mxnet/pull/10391#discussion_r179344795
########## File path: docs/tutorials/python/float16.md ########## @@ -0,0 +1,280 @@ +# Mixed precision training using float16 + +The computational resources required for training deep neural networks has been increasing of late because of complexity of the architectures and size of models. Mixed precision training allows us to reduces the resources required by using lower precision arithmetic. In this approach we train using 16 bit floating points (half precision) while using 32 bit floating points (single precision) for output buffers of float16 computation. This combination of single and half precision gives rise to the name Mixed precision. It allows us to achieve the same accuracy as training with single precision, while decreasing the required memory and training or inference time. Review comment: gives rise to the name Mixed precision: why capital M? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
