5 3 14

Logesh Kumar umapathi

infinitylogesh

http://www.logeshumapathi.com

AI & ML interests

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

Recent Activity

liked a Space 21 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

liked a Space 21 days ago

2.24k

The Ultra-Scale Playbook

🌌

The ultimate guide to training LLM on large GPU Clusters

liked a Space 10 months ago

Fluently Playground v0.25

😼

Generate images on modern models of the Fluently family

liked 2 models 11 months ago

HuggingFaceM4/idefics2-8b

Image-Text-to-Text • Updated Oct 14, 2024 • 19k • • 602

stas/ml-engineering-book

Updated Jan 22 • 16

liked a model about 1 year ago

NousResearch/Genstruct-7B

Text Generation • Updated Mar 7, 2024 • 5.27k • 373

liked 3 datasets over 1 year ago

liked a Space about 2 years ago

169

SantaCoder Demo

🎅

Generate code snippets in Python, Java, JavaScript

liked a model about 2 years ago

michiyasunaga/BioLinkBERT-base

Text Classification • Updated Mar 31, 2022 • 4.85k • 36

liked a model over 2 years ago

CarperAI/FIM-NeoX-1.3B

Text Generation • Updated Oct 11, 2022 • 112 • 26

liked 2 Spaces over 2 years ago

533

GLM 130B

📚

110

Incoder Demo

💻

liked a Space over 3 years ago

Neuspell Demo

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