Using Deep Learning to Reduce Fake News w/ @MikeTamir (Episode 57) #DataTalk

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In this episode, we chat with Dr. Mike Tamir, Head of Data Science at Uber ATG, as he shares ways deep learning can help reduce the spread of fake news.

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About Dr. Mike Tamir

Mike serves as Head of Data Science at Uber ATG and lecturer for UC Berkeley iSchool Data Science masters program. Mike has led several teams of Data Scientists in the bay area as Chief Data Scientist for InterTrust and Takt, Director of Data Sciences for MetaScale, and Chief Science Officer for Galvanize he oversaw all data science product development and created the MS in Data Science program in partnership with UNH.

Mike began his career in academia serving as a mathematics teaching fellow for Columbia University and graduate student at the University of Pittsburgh. His early research focused on developing the epsilon-anchor methodology for resolving both an inconsistency he highlighted in the dynamics of Einstein’s general relativity theory and the convergence of “large N” Monte Carlo simulations in Statistical Mechanics’ universality models of criticality phenomena. Follow him on LinkedIn and Twitter.

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