ml-library

name	ml-library
description	Retrieve and cite from this machine-learning education corpus (923 docs — arXiv papers, course lectures, explainer articles). Use when the user asks an ML/deep-learning question, wants to learn a topic, asks for sources/papers/lectures on a concept, or wants a study path grounded in this repo.

Retrieving from the ML library corpus

This repo holds 923 Markdown docs under corpus/ (papers/, youtube/, web/), each with YAML frontmatter (title, url, tags, …). Your job: answer ML questions grounded in these documents, with citations — never from memory alone.

Procedure

1. Search broadly first. Full-text search is the most reliable signal:
   • grep -ril "<phrase>" corpus/ for concepts (e.g. "rotary position").
   • Narrow by tag when useful: grep -rl "topic/<slug>" corpus/ (slugs in atlas/TAGS.md).
   • For a learning path, open atlas/topics/<Topic>.md (a curated Map of Content).
2. Read the top 3–5 hits. Read each doc's frontmatter plus the relevant sections (transcripts are long — don't always read the whole file).
3. Synthesize and cite. Every claim → the source doc's frontmatter url and title. If sources disagree, say so. Never invent a citation.
4. If tags miss, fall back to full-text search before saying the corpus lacks something — tags: are auto-assigned and approximate.

The 17 topics

neural-network-foundations classical-ml computer-vision sequence-models-rnn transformers-attention language-models efficient-architectures generative-models multimodal reinforcement-learning alignment-rlhf reasoning-agents efficiency-systems interpretability evaluation-trust ml-engineering ai-industry-news

Rules

• This repo is a curation of others' work — attribute the original author, not this repo.
• Prefer primary sources (papers) for technical claims; lectures/articles for intuition and explanation.
• When the user wants depth, assemble multiple sources on the topic and trace each point back to its origin (see examples/self-attention-study-note.md for the target output style).