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Machine Learning in Academia and Industry
Recorded at:

| by Deborah Hanus Follow 1 Followers on Oct 10, 2017 | NOTICE: The next QCon is in London, Mar 4 - 6, 2019. Join us!
40:26

Summary
Deborah Hanus discusses some of the challenges that can arise when working with data. With recent advances in computational power, ML is positioned to change interaction with the world around. A surge of well-maintained ML libraries has made it possible for engineers to use ML models with minimal background. However, many find that using ML responsibly can be harder than it seems.

Bio

Deborah Hanus is a PhD candidate studying machine learning in Harvard University's Computer Science Department. She has worked as an engineer at a San Francisco start-up and Google. She has been awarded the Fulbright Student Fellowship, NSF Graduate Research Fellowship, and the Intel/ACM SIGHPC Computational Data Science Fellowship.

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