In the last video on reinforcement learning, we look into the deep reinforcement learning techniques. We start looking into how Deep Mind beat Atari Games and in particular breakout. Furthermore, we look into the technology behind AlphaGo and AlphaGoZero to play Go, Chess, and Shogi on world-class ...
This video discusses several other solution approaches to learning games such as Monte Carlo Techniques, Temporal Difference Learning, Q Learning, and learning universal function approximators for reinforcement learning using the policy gradient.
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This video explains a fundamental learning technique in reinforcement learning: Policy Iteration.
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Read the Transcript (Summer 2020) at:LMETowards Data Science
This video explains the basics of reinforcement learning: The Markov Decision Process and how to compute the expected future return.
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Read the Transcript (Summer 2020) at:LMETowards Data Science
This video explains the concepts of sequential decision making and the multi-armed bandit problem.
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Read the Transcript (Summer 2020) at:LMETowards Data Science
This video explains the concepts of attention in deep learning.
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This is a video for everybody who is also interested in more philosophical topics such as conscious and self-aware machines. We did a small review of the state-of-the-art and present what we found.
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Read the Paper at:ArxivFrontiers on Computational Neuroscience
This video introduces gradient and optimization based visualization.
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Read the Transcript (Summer 2020) at:LMETowards Data Science
This video shows simple visualization techniques based on lesion studies and investigating activations.
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Read the Transcript (Summer 2020) at:LMETowards Data Science
This video motivates why we need visualization techniques.
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Read the Transcript (Summer 2020) at:LMETowards Data Science