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ATOM00blue

ATOM00blue/machine-learning-library

A curated, Markdown-based ML knowledge base of 923 papers, lectures and articles, plus a small NumPy-only custom classifier and regressor called APRL.

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마지막 커밋
2개월 전

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The repository combines two layers. The first is APRL (Adaptive Prototype Residual Learning), an experimental classifier and regressor for numeric tabular data. It pairs a global ridge model with relevance-weighted prototypes and local polynomial experts that correct residual errors, and it offers distance-aware uncertainty, missing-value handling and incremental batch updates on a NumPy-only runtime. The project presents it as an experimental composition rather than a state-of-the-art method.

The second layer is a knowledge base of about 11 million tokens. It contains 391 arXiv papers (78 full text, 313 abstract plus metadata), 474 lecture transcripts from courses such as MIT 6.S191, Stanford CS224n and CS231n, and 58 web explainers. Every file is Markdown with YAML frontmatter that records source, URL, authors and controlled topic tags. The corpus is organized into a 17-topic map and can be opened as an Obsidian vault.

Both layers are meant to be used together or separately: you can train and test APRL, read or grep the corpus, or build a retrieval system over it that cites specific papers and lectures.

핵심 기능

  • 01Custom APRL classifier and regressor for numeric tabular data, implemented with NumPy only.
  • 02Distance-aware uncertainty estimates and missing-value handling in the APRL model.
  • 03Corpus of 923 Markdown documents spanning papers, lecture transcripts and articles.
  • 04Every file carries YAML frontmatter with source, URL, authors and controlled topic tags.
  • 05Organized into a 17-topic map with hub pages and reading paths, usable as an Obsidian vault.

활용 사례

  • Learning ML fundamentals and frontier research through lectures and papers with consistent metadata.
  • Building a retrieval-augmented Q&A assistant that cites specific papers or lectures.
  • Experimenting with a small, inspectable tabular classifier or regressor with uncertainty estimates.
  • Using the clean corpus as a text dataset for embedding benchmarks or model fine-tuning.

토픽

#machine-learning#study-resources#dataset#nlp#llm#education#arxiv#deep-learning#corpus#transformers

자주 묻는 질문

machine-learning-library은(는) 무엇인가요?
A curated, Markdown-based ML knowledge base of 923 papers, lectures and articles, plus a small NumPy-only custom classifier and regressor called APRL. The repository combines two layers. The first is APRL (Adaptive Prototype Residual Learning), an experimental classifier and regressor for numeric tabular data. It pairs a global ridge model with relevance-weighted prototypes and local polynomial experts that correct residual errors, and it offers distance-aware uncertainty, missing-value handling and incremental batch updates on a NumPy-only runtime. The project presents it as an experimental composition rather than a state-of-the-art method.
machine-learning-library은(는) 무료로 사용할 수 있나요? 라이선스는 무엇인가요?
machine-learning-library은(는) Other 라이선스의 오픈소스입니다. 상업용 제품에 사용하기 전에 라이선스 조건을 확인하세요.
machine-learning-library은(는) 아직 유지보수되고 있나요?
6개월 이내 업데이트. machine-learning-library의 마지막 커밋은 2026년 8월 10일입니다.
machine-learning-library은(는) 어디에 사용할 수 있나요?
Learning ML fundamentals and frontier research through lectures and papers with consistent metadata. Building a retrieval-augmented Q&A assistant that cites specific papers or lectures. Experimenting with a small, inspectable tabular classifier or regressor with uncertainty estimates. Using the clean corpus as a text dataset for embedding benchmarks or model fine-tuning.
machine-learning-library은(는) 어디서 받을 수 있나요?
소스 코드와 설치 방법은 GitHub에 있습니다: https://github.com/ATOM00blue/machine-learning-library

요약은 프로젝트 README를 바탕으로 AI가 작성했습니다. GitHub 통계는 매일 갱신됩니다.

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