“MXNet, born and bred here at CMU, is the most scalable framework for deep learning I have seen and is a great example of what makes this area of computer science so beautiful – that you have different disciplines which all work so well together: imaginative linear algebra working in a novel way with massive distributed computation leading to a whole new ball game for deep learning,” said Andrew Moore, former dean of Computer Science at the Carnegie Mellon University.

Read more: https://analyticsindiamag.com/can-mxnet-stand-up-to-tensorflow-pytorch/

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