This repository serves as a personal learning journal for advanced machine learning topics, primarily targeting intermediate to advanced practitioners. It offers in-depth notes and curated resources on subjects like Mechanistic Interpretability, LLMs, and Graph Neural Networks, aiming to provide a structured learning path beyond foundational knowledge.

How It Works

The journal is structured chronologically by "Days," with a table of contents mapping specific subjects to relevant day ranges. The author's approach involves deep dives into fundamentals, supplemented by a curated list of essential readings and viewings from prominent researchers and AI labs, facilitating a comprehensive understanding of complex ML concepts.

Highlighted Details

• Focus on Mechanistic Interpretability as the current learning objective.
• Covers topics including LLM Hallucination, GNNs, and AlphaFold 3.
• Curated learning resources from Ilya Sutskever, Distilled AI, Artem Kirsanov, and Andrej Karpathy.
• Aims for in-depth understanding and implementation of Transformers and LLMs.

Maintenance & Community

This is a personal learning journal, and there is no indication of active community contribution or formal maintenance beyond the author's personal progress.

Licensing & Compatibility

The repository does not specify a license.

Limitations & Caveats

This repository is a personal learning journal and not a production-ready library or framework. Content is subject to the author's ongoing learning process and may not represent the most current or universally accepted understanding of the topics.