TensorFlow Probability (TFP) is a TF/Python library offering a modern take on both emerging and traditional probability and statistics tools. Statisticians and data scientists will find R-like capabilities, which naturally leverage modern hardware, while ML researchers and practitioners will find powerful building blocks for specifying and learning deep, probabilistic models.
Joshua Dillon and Wolff Dobson discuss core TFP abstractions and demo some of TFP’s modeling power and convenience. They also share some of the recent results from Project Magenta, a research project exploring the role of machine learning in the process of creating art and music.
Joshua V. Dillon is a software engineer for research and machine intelligence at Google. His research interests include approximate inference techniques for probabilistic models, uncertainty in machine learning, and designing probabilistic programming tools and languages. He holds a PhD in machine learning from the Georgia Institute of Technology. In his free time, Josh enjoys spending time with his family, cycling, and woodworking.
Wolff Dobson is a developer programs engineer at Google specializing in machine learning and games. Previously, he worked as a game developer, where his projects included writing AI for the NBA 2K series and helping design the Wii Motion Plus. Wolff holds a PhD in artificial intelligence from Northwestern University.
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