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Logesh Kumar umapathi

infinitylogesh
moresearch's profile picture 21world's profile picture
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http://www.logeshumapathi.com

AI & ML interests

NLP - Healthcare , Information retrieval , Open domain question answering.

Recent Activity

liked a Space about 1 month ago
nanotron/ultrascale-playbook
reacted to Kseniase's post with ❤️ about 2 months ago
8 Free Sources on Reinforcement Learning With the phenomenon of DeepSeek-R1's top reasoning capabilities, we all saw the true power of RL. At its core, RL is a type of machine learning where a model/agent learns to make decisions by interacting with an environment to maximize a reward. RL learns through trial and error, receiving feedback in the form of rewards or penalties. Here's a list of free sources that will help you dive into RL and how to use it: 1. "Reinforcement Learning: An Introduction" book by Richard S. Sutton and Andrew G. Barto -> https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf 2. Hugging Face Deep Reinforcement Learning Course -> https://huggingface.co/learn/deep-rl-course/unit0/introduction You'll learn how to train agents in unique environments, using best libraries, share your results, compete in challenges, and earn a certificate. 3. OpenAI Spinning Up in Deep RL -> https://spinningup.openai.com/en/latest/index.html A comprehensive overview of RL with many useful resources 4. "Reinforcement Learning and Optimal Control" books, video lectures and course material by Dimitri P. Bertsekas from ASU -> https://web.mit.edu/dimitrib/www/RLbook.html Explores approximate Dynamic Programming (DP) and RL with key concepts and methods like rollout, tree search, and neural network training for RL and more. 5. RL Course by David Silver (Google DeepMind) -> https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPeb Many recommend these video lectures as a good foundation 6. RL theory seminars -> https://sites.google.com/view/rltheoryseminars/home?authuser=0 Provides virtual seminars from different experts about RL advancements 7. "Reinforcement Learning Specialization" (a 4-course series on Coursera) -> https://www.coursera.org/learn/fundament 8. Concepts: RLHF, RLAIF, RLEF, RLCF -> https://www.turingpost.com/p/rl-f Our flashcards easily explain what are these four RL approaches with different feedback
authored a paper 4 months ago
MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering
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infinitylogesh's activity

upvoted a collection 5 months ago
upvoted a paper about 1 year ago
upvoted a collection about 1 year ago