AI & DEEP LEARNING
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Date: Wednesday, June 26, 2019
Time: 9:00am – 10:00am PT
Duration: 1 hour

RAPIDS is an open-source platform, incubated at NVIDIA, for GPU-accelerated data science. It’s transforming many areas of the financial services industry, including the performance record for a representative benchmark designed to evaluate platforms for backtesting trading strategies.

This talk is targeted at data scientists familiar with PyData tools and working in the financial services industry. RAPIDS is created with an API that will immediately look and feel familiar to PyData users of tools like pandas and scikit-learn. Participants can expect to leave this webinar knowing how to do what they already do, but with the competitive advantage of doing it on GPUs and going 50x - 6,000x faster.

In this webinar you will learn:
  • about the RAPIDS platform and how financial institutions are leveraging the platform
  • the applications in both asset management, and retail banking
  • how to get started and where the RAPIDS near-term roadmap is going

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DGX Station Datasheet

Get a quick low-down and technical specs for the DGX Station.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.

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Speaker

Paul Mahler

Senior Data Scientist, NVIDIA

Paul Mahler is a Senior Data Scientist at NVIDIA in Boulder, CO. At NVIDIA, Paul’s focus has been on building tools that accelerate data science workflows by leveraging the power of GPU technology. Prior to NVIDIA, Paul worked as a data scientist at a Fin Tech start-up in San Francisco, delivering predictive models around risk and credit worthiness. Paul’s career also included time at Accenture, Fannie Mae, and the World Bank.

Paul Mahler is a Senior Data Scientist at NVIDIA in Boulder, CO. At NVIDIA, Paul’s focus has been on building tools that accelerate data science workflows by leveraging the power of GPU technology. Prior to NVIDIA, Paul worked as a data scientist at a Fin Tech start-up in San Francisco, delivering predictive models around risk and credit worthiness. Paul’s career also included time at Accenture, Fannie Mae, and the World Bank.

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Date & Time: Wednesday, April 22, 2018