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Artificial intelligence using GPU

Kurt Schmidt and Jeff Layton
Thursday, October 26, 2017 - 12:00pm to 1:00pm
Student Center East Room 217

Data scientists in both industry and academia have been using GPUs for AI and machine learning to make groundbreaking improvements across a variety of applications including image classification, video analytics, speech recognition and natural language processing. In particular, Deep Learning - the use of sophisticated, multi-level "deep" neural networks to create systems that can perform feature detection from massive amounts of unlabeled training data - is an area that has been seeing significant investment and research. Although AI has been around for decades, two relatively recent trends have sparked widespread use of Deep Learning within AI: the availability of massive amounts of training data, and powerful and efficient parallel computing provided by GPU computing. Early adopters of GPU accelerators for machine learning include many of the largest web and social media companies, along with top tier research institutions in data science and machine learning. With thousands of computational cores and 10-100x application throughput compared to CPUs alone, GPUs have become the processor of choice for processing big data for data scientists.

Speaker's Bio: 

Jeff Layton is a Senior Solution Architect in the Worldwide Field Organization and a Certified Deep Learning Institute (DLI) Instructor at NVIDIA. His primary roles are to support high performance computing and deep learning with AI. He is focused on applying Deep Learning within Artificial Intelligence. Prior to joining NVIDIA, Jeff spent time at Amazon Web Services and Dell providing high performance computing architecture and computational science support to government and educational organizations. Jeff holds a Ph.D. in Aeronautical and Astronautical Engineering from Purdue University. He is also an active contributing writer to ADMIN Magazine, ADMIN Magazine ( ) as well as HPC ADMIN Magazine (, in addition to Quinstreet ( ).