AI-Papers-of-the-Week

Focusing on Deep Learning specifically: - Most LLMs currently use the transformer architecture. You can learn about this visually (https://bbycroft.net/llm), or through this blog post (https://jalammar.github.io/illustrated-transformer/), or through any number of Andrej Karpathy's blog posts and materials. - To stay on top of papers that get published every week, I read a summary every Sunday: https://github.com/dair-ai/ML-Papers-of-the-Week - To learn more about the engineering side of it, you can join Discord servers such as EleutherAI's, or follow GitHub discussions of projects like llama.cpp Personally I think the best way to develop per unit time is probably to try to re-implement some of the big papers in the field. There's a clear goal, there are clear signs of success, there are many implementations out there for you to check your

- Top ML papers of the week: https://github.com/dair-ai/ML-Papers-of-the-Week - Daily Newsletter on AI: https://tldr.tech/ai - Subreddit about open-source LLM advancements: https://www.reddit.com/r/LocalLLaMA/

This is what works for me so far: - This repo has a list of important papers at the end of every week: https://github.com/dair-ai/ML-Papers-of-the-Week - The best papers are usually discussed pretty quickly on relevant Discord servers I'm in (HuggingFace, EleutherAI, FastAI, etc). - I follow discussions on a few GitHub repos and learn some implementation details that way. (llama.cpp, oobabooga, etc) - Finally- there are quite a few YouTube channels that are good supplements (there was a good HN thread about this a while ago). I'd also like to offer some encouragement- I remember feeling daunted at the beginning, thinking of how fast things were moving in AI, and how slow I'd be in comparison. But once I started reading papers, it became a lot easier to make connections and identify which papers were actually novel and exciting versus whic