AI CURRICULUM

We created a small repository linking to open Deep Learning and Reinforcement Learning lectures provided by MIT, Stanford University and UC Berkeley. ⚪ MIT 6.S191: Introduction to Deep Learning | 2020⚪ CS231n: CNNs for Visual Recognition, Stanford | Spring 2019⚪ CS224n: NLP with Deep Learning, Stanford | Winter 2019⚪ CS285: Deep Reinforcement Learning, UC Berkeley | … Continue reading AI CURRICULUM

INTRO TO REINFORCEMENT LEARNING

We finally kicked off our MLT Reinforcement Learning sessions with an "Intro to RL" study group at the Tokyo Metropolitan Library. Anugraha Sinha walked us through the following: Theory Introduction to RL Important elements of an RL problem Description of Markov Decision Process (MDP) and and Markov Assumption. Importance of parametrization of State, Action, Reward … Continue reading INTRO TO REINFORCEMENT LEARNING