NIPS Day 1: Tutorials on Scaling Deep Learning, Probabilistic Programming, and Reinforcement Learning
I'm at my first annual NIPS conference this year in Montreal, the annual pow-wow for machine learning and deep learning in particular.
Monday, the first day, had several multi-hour in-depth tutorials from literally the folks that wrote the textbooks in these areas. I attended sessions on scaling deep learning via TensorFlow, presented by Google folks like Jeff Dean; a deep dive into probabilistic programming (being able to describe a statistical system and allow an inference engine to do the hard work of building a model from it); and an introduction to reinforcement learning (using a scalar reward signal to automatically discover the optimal policy for a behavior).
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