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Practical Deep Learning for Data Scientists


This tutorial is a chance to get hands-on with PyTorch and GPU Deep Learning (DL). It is specifically targeted toward attendees who may be familiar with the concepts of DL, but want practical experience. Familiarity with Python and typical ML packages (e.g. pandas, numpy, sklearn) is expected.

At the end of this session, you will understand how to: Build some common DL architectures in PyTorch Evaluate and improve the performance Take advantage of more compute (and when you should do so)

This will set you up to take advantage of interesting developments in the field and maybe even contribute your own!


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