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DTSTART;TZID=America/Chicago:20181112T083000
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UID:submissions.supercomputing.org_SC18_sess259_tut177@linklings.com
SUMMARY:Deep Learning at Scale
DESCRIPTION:Steven A. Farrell and Deborah Bard (Lawrence Berkeley National
  Laboratory), Michael F. Ringenburg (Cray Inc), and Thorsten Kurth and Mr 
 Prabhat (Lawrence Berkeley National Laboratory)\n\nDeep learning is rapidl
 y and fundamentally transforming the way science and industry use data to 
 solve problems. Deep neural network models have been shown to be powerful 
 tools for extracting insights from data across a large number of domains. 
 As these models grow in complexity to solve increasingly challenging probl
 ems with larger and larger datasets, the need for scalable methods and sof
 tware to train them grows accordingly.\n\nThe Deep Learning at Scale tutor
 ial aims to provide attendees with a working knowledge of deep learning on
  HPC class systems, including core concepts, scientific applications, and 
 techniques for scaling.  We will provide training accounts and example Jup
 yter notebook-based exercises, as well as datasets, to allow attendees to 
 experiment hands-on with training, inference, and scaling of deep neural n
 etwork machine learning models.\n\nTag: Deep Learning, Machine Learning, T
 ools\n\nRegistration Category: Tutorial Reg Pass\n\n
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