Accelerating Deep Learning with GPUs

Body Copy ScikitLearn, NumPy, and pandas form a great toolkit for singlemachine, inmemory analytics, but scaling them to larger datasets can be difficult.
In a live webinar on Thursday, June 21, at 2PM CT, watch Anaconda Data Scientist Tom Augspurger demonstrate how dask enables analysis of large datasets in parallel, using all the cores of your laptop or all the machines in your cluster.
Tom will highlight daskml, a library for scalable machine learning, and show you how daskml can train estimators on large datasets.

Meet Our Speaker

  • Stan Seibert, Director of Community Innovation

    Stan leads the Community Innovation team at Anaconda, where his work focuses on high-performance GPU computing and designing data analysis, simulation, and processing pipelines. He is a longtime advocate of the use of Python and GPU computing for research. Prior to joining Anaconda, Stan served as Chief Data Scientist at Mobi, where he worked on vehicle fleet tracking and route planning.

    Stan received a PhD in experimental high energy physics from the University of Texas at Austin and performed research at Los Alamos National Laboratory, University of Pennsylvania, and the Sudbury Neutrino Observatory.

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