This repository provides a comprehensive curriculum for advanced machine learning topics, including transformer interpretability, reinforcement learning, and LLM evaluations. It is designed for researchers and practitioners interested in understanding and building complex AI systems, offering hands-on exercises and practical implementations.

How It Works

The program is structured into distinct chapters, each focusing on a specific area of AI. It utilizes Python for implementation, with a strong emphasis on practical coding exercises. Key libraries like TransformerLens and OpenAI's Gym are integrated for tasks such as building transformers from scratch, analyzing their internal mechanisms, and implementing reinforcement learning agents. The curriculum guides users through building and fine-tuning neural networks, implementing backpropagation, and exploring generative models like GANs and VAEs.

Quick Start & Requirements

• Install via git clone https://github.com/callummcdougall/ARENA_3.0.git and run ARENA_3.0/install.sh.
• Prerequisites include Python and standard ML libraries. Specific exercises may require GPU acceleration and CUDA for advanced tasks.
• Further details and virtual study materials are available on a linked Notion page.

Highlighted Details

• Chapter 0 covers fundamentals like building CNNs, Residual Networks, backpropagation, GANs, and VAEs.
• Chapter 1 focuses on transformer interpretability, including building transformers, using TransformerLens, and replicating research on superposition.
• Chapter 2 delves into reinforcement learning, covering multi-armed bandits, DQN, PPO, and RLHF.
• Chapter 3 provides hands-on experience with LLM evaluations, including MCQ benchmarks and agent-based evaluations using tools like Inspect.

Maintenance & Community

The project is actively maintained, with instructions for submitting Pull Requests (PRs) provided. Users are encouraged to contribute via PRs, particularly to the master Python files in infrastructure/master_files. A Slack channel #errata is available for support and discussions.

Licensing & Compatibility

The repository's license is not explicitly stated in the provided README. Users should verify licensing terms for commercial use or integration into closed-source projects.

Limitations & Caveats

While the README outlines extensive content, specific hardware requirements (e.g., GPU, CUDA versions) for certain advanced exercises are not detailed upfront. Some sections are marked as optional, requiring users to select their learning path.