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Personalization and Scalable Deep Learning with MXNET

The presentation below by Alex Smola is “Personalization and Scalable Deep Learning with MXNET” from the MLconf San Francisco, 2016. User return times and movie preferences are inherently time dependent. In this talk, Alex shows how this can be accomplished efficiently using deep learning by employing an LSTM (Long Short Term Model). Moreover, he shows how to train large scale distributed parallel models using MXNet efficiently. This includes a brief overview of key components of defining networks, of optimization, and a walk through of the steps required to allocate machines, and to train a model.

Alex Smola is the Manager of the Cloud Machine Learning Platform at Amazon. Prior to his role at Amazon, Smola was a Professor in the Machine Learning Department of Carnegie Mellon University and cofounder and CEO of Marianas Labs. Prior to that he worked at Google Strategic Technologies, Yahoo Research, and National ICT Australia. Prior to joining CMU, he was professor at UC Berkeley and the Australian National University. Alex obtained his PhD at TU Berlin in 1998. He has published over 200 papers and written or coauthored 5 books.

 

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