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Logesh Kumar umapathi

infinitylogesh

http://www.logeshumapathi.com

AI & ML interests

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

Recent Activity

liked a Space 20 days ago

nanotron/ultrascale-playbook

reacted to Kseniase's post with ❤️ about 1 month 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

New activity in hywu/Camelidae-8x34B 12 months ago

How is the support for TGI and VLLM ?

#3 opened about 1 year ago by

infinitylogesh

commented a paper 12 months ago

Executable Code Actions Elicit Better LLM Agents

Paper • 2402.01030 • Published Feb 1, 2024 • 97 •

New activity in abacaj/mistral-7b-sft over 1 year ago

Dataset

#1 opened over 1 year ago by

infinitylogesh

New activity in mistralai/Mistral-7B-v0.1 over 1 year ago

Cant run the model with the most basic code

#7 opened over 1 year ago by

masterchop

New activity in maderix/llama-65b-4bit almost 2 years ago

Example of how to use the models

#3 opened about 2 years ago by

milyiyo