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Deep Reinforcement Learning Series – Part 3: function approximators
May 20, 2017 @ 10:00 am - 12:00 pm
Join us as Metis hosts Deep Reinforcement Learning Series – Part 3: function approximators with Jeremy Watt!
In this series of tutorial talks, we will be Deep Reinforcement Learning from start to finish – the tech powering self-playing Atari games, Alpha Go, problems in automatic control and more.
In Part 3 of the series we will be covering extensions of Q-Learning to problems – like chess, control, and video games – where the enormous size of the state space makes resolving Q – directly – impossible. As cycling through even a reasonable portion of the states is computationally impossible, function approximators are introduced to greatly generalize the effect of a Q-Learner.
This talk will be highly interactive with a number of live code demonstrations and a fully featured Jupyter Notebook.
Breakfast will be provided.
If you did not attend previous events in this series be sure to check out the resources listed in those events – including slides, blog posts, etc.,
Blog posts summarizing much of the previous events in this series can be found below!
We’re also working on a blog series on nonlinear learning.
You can find the first post in this series below:
Metis (thisismetis.com) accelerates careers in data science by providing full-time immersive bootcamps, evening part-time professional development courses, online resources, and corporate programs based in Seattle, New York, Chicago, and San Francisco.
Brought to you by Kaplan, Metis focuses primarily on Python, machine learning, data visualization, deep learning, big data processing, statistical foundations, and more. Students and alumni of the bootcamp program receive continuous support from our career advisors, empowering them to pursue a successful career in the fast-growing field of data science.
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