Presented By O'Reilly and Cloudera
Make Data Work
March 28–29, 2016: Training
March 29–31, 2016: Conference
San Jose, CA
John Canny

John Canny
Professor, UC Berkeley

Website

John F. Canny is a computer scientist and the Paul and Stacy Jacobs Distinguished Professor of Engineering in the Computer Science Department of the University of California, Berkeley. John has made significant contributions in various areas of computer science and mathematics, including artificial intelligence, robotics, computer graphics, human-computer interaction, computer security, computational algebra, and computational geometry.

Sessions

11:30am–12:00pm Tuesday, 03/29/2016
Hardcore Data Science
Location: 210 C/G
John Canny (UC Berkeley)
Average rating: ****.
(4.07, 14 ratings)
GPUs have proven their value for machine learning, offering orders-of-magnitude speedups on dense and sparse data. They define the current performance limits for machine learning but have limited model capacity. John Canny explains how to mitigate that challenge and achieve linear speedups with GPUs on commodity networks. The result defines the hitherto unseen "outer limits" of ML performance. Read more.