This repository serves as a curated collection of papers, code, and resources for a deep learning study group. It aims to provide a structured learning path through seminal and recent research in artificial intelligence and machine learning, catering to individuals seeking to deepen their understanding of advanced AI concepts.

How It Works

The repository is organized chronologically by meeting date, with each entry linking to a specific research paper (primarily from arXiv), often accompanied by relevant blog posts, YouTube videos, and code repositories. This structure facilitates a systematic exploration of diverse topics within deep learning, from foundational concepts to cutting-edge advancements.

Quick Start & Requirements

• Access: No installation required; browse the README for paper links and resources.
• Prerequisites: Internet access to view linked papers, videos, and code.
• Setup: Instantaneous.

Highlighted Details

• Extensive historical coverage of key AI research papers from 2016 to late 2023.
• Links to official code repositories and explanatory videos for many papers.
• Covers a broad spectrum of deep learning subfields including transformers, diffusion models, reinforcement learning, graph neural networks, and more.
• Includes links to community resources like Discord for discussion.

Maintenance & Community

• The repository is maintained by mike-bowles.
• Community discussion is encouraged via the linked Discord server.

Licensing & Compatibility

• Licensing is not specified for the repository itself, but it links to external research papers and codebases, each with their own licenses. Users should verify compatibility for any derived use.

Limitations & Caveats

This repository is a curated list of links and does not contain executable code or models itself. The depth of coverage for each topic varies, and users are expected to independently explore the linked resources.