Abstract: In this talk we explore real world use case applications for automated “Fake News” evaluation using contemporary deep learning article vectorization and tagging. We begin with the use case and an evaluation of the appropriate context applications for various deep learning applications in fake news evaluation. Technical material will review several methodologies for article vectorization with classification pipelines, ranging from traditional to advanced deep architecture techniques. We close with a discussion on troubleshooting and performance optimization when consolidating and evaluating these various techniques on active data sets.
Bio: Mike serves as Head of Data Science at Uber ATG, UC Berkeley Data Science faculty, and head of Skymind Labs the Machine Learning research lab affiliated with DeepLearning4J. He has led teams of Data Scientists in the bay area as Chief Data Scientist for InterTrust and Takt, Director of Data Sciences for MetaScale/Sears, and CSO for Galvanize where he founded the galvanizeU-UNH accredited Masters of Science in Data Science degree and oversaw the company's transformation from co-working space to Data Science organization.
Michael Tamir, PhD
Head of Data Science at Uber