Presented By O'Reilly and Cloudera
Make Data Work
March 13–14, 2017: Training
March 14–16, 2017: Tutorials & Conference
San Jose, CA

Schedule: AI sessions

Underneath all the AI hype, real breakthroughs are happening—and obstacles to applied AI are being overcome. Check out these AI-related sessions at Strata + Hadoop World in San Jose and get a head start on incorporating AI into your projects.

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9:00am12:30pm Tuesday, March 14, 2017
Location: LL20 C
Edd Wilder-James (Google), Ellen Friedman (Independent), Jim Scott (MapR Technologies), Gabriela de Queiroz (R-Ladies), Melanie Warrick (Google), Aneesh Karve (Quilt)
Data 101 introduces you to core principles of data architecture, teaches you how to build and manage successful data teams, and inspires you to do more with your data through real-world applications. Setting the foundation for deeper dives on the following days of Strata + Hadoop World, Data 101 reinforces data fundamentals and helps you focus on how data can solve your business problems. Read more.
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2:40pm3:20pm Wednesday, March 15, 2017
James Bradbury (Salesforce Research)
Average rating: ****.
(4.00, 8 ratings)
James Bradbury offers an overview of PyTorch, a brand-new deep learning framework from developers at Facebook AI Research that's intended to be faster, easier, and more flexible than alternatives like TensorFlow. James makes the case for PyTorch, focusing on the library's advantages for natural language processing and reinforcement learning. Read more.
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9:00am9:15am Thursday, March 16, 2017
Law, ethics, governance
Location: Grand Ballroom
Andra Keay (Silicon Valley Robotics)
Average rating: ****.
(4.09, 46 ratings)
Let’s stop talking about bad robots and start talking about what makes a robot good. A good or ethical robot must be carefully designed. Andra Keay outlines five principles of good robot design and discusses the implications of implicit bias in our robots. Read more.
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11:00am11:40am Thursday, March 16, 2017
Rajat Monga (Google)
Average rating: ***..
(3.86, 7 ratings)
Rajat Monga offers an overview of TensorFlow progress and adoption in 2016 before looking ahead to the areas of importance in the future—performance, usability, and ubiquity—and the efforts TensorFlow is making in those areas. Read more.
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11:50am12:30pm Thursday, March 16, 2017
Data science & advanced analytics
Location: 230 A Level: Beginner
Michael Lee Williams (Fast Forward Labs)
Average rating: ***..
(3.80, 5 ratings)
Real-world data is incomplete and imperfect. The right way to handle it is with Bayesian inference. Michael Williams demonstrates how probabilistic programming languages hide the gory details of this elegant but potentially tricky approach, making a powerful statistical method easy and enabling rapid iteration and new kinds of data-driven products. Read more.