# dair-ai/ML-Papers-Explained

Explanation to key concepts in ML

Repository: https://github.com/dair-ai/ML-Papers-Explained
Canonical: https://ross.abutalabs.com/products/ml-papers-explained
License Family: other
Last push: 2025-06-30T03:35:10+00:00

## Health v2 (maintenance only)
Score: 44/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 29, release rhythm 35, longevity 94
- inputs: {"age_days": 1329, "days_push": 429, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8595, forks 700 (observed 2026-08-28T04:10:23.719855+00:00)

## What it is
A curated collection of plain-language explanations of key machine learning papers and concepts, organized into tables covering language models and other ML topics. Each entry links to a detailed article explaining the paper's contributions.

## Use cases
- understand how the transformer architecture works
- get a summary of the BERT paper
- learn differences between GPT versions
- study key ML papers before an interview
- find reading material on attention mechanisms
- catch up on language model research

## When to choose
- you want concise, accessible explanations of influential ML papers
- you are building a study plan for ML/deep learning concepts
- you need quick refreshers on models like BERT, GPT, or XLNet

## When to avoid
- you need runnable code or implementations
- you want rigorous peer-reviewed material or full paper texts
- you need exhaustive coverage of every ML subfield

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, machine-learning, nlp
- domain: machine-learning, deep-learning, large-language-models, tutorials
- platform: -
- tags: papers-explained, study-notes, ml-concepts, curated-list, educational, natural-language-processing, web-server

## Member repositories
- dair-ai/ML-Papers-Explained (main) score 44

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:23.719855+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:26:08.755828+00:00, confidence not recorded.
  - readme: https://github.com/dair-ai/ML-Papers-Explained (fetched 2026-08-28T04:10:23.719855+00:00, sha 43776cf61f95)
- Data as of 2026-08-30T08:39:29.467469+00:00.