# dennybritz/deeplearning-papernotes

Summaries and notes on Deep Learning research papers

Repository: https://github.com/dennybritz/deeplearning-papernotes
Canonical: https://ross.abutalabs.com/products/deeplearning-papernotes
License Family: other
Last push: 2018-02-13T01:04:02+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3910, "days_push": 3124, "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 4420, forks 896 (observed 2026-08-28T04:08:48.744843+00:00)

## What it is
A curated collection of summaries and personal notes on deep learning research papers, organized chronologically with links to arXiv, articles, and code. It serves as a reading companion for staying current with ML research.

## Use cases
- find summaries of deep learning research papers
- keep up with new ML papers each month
- get quick overviews before reading full papers
- discover papers with linked code implementations
- build a deep learning reading list

## When to choose
- you want concise human-written summaries of DL papers
- you need a curated chronological paper feed with code links
- you're studying deep learning and want guided reading

## When to avoid
- you need coverage of papers after early 2018
- you want exhaustive or peer-reviewed paper reviews
- you need a structured course or tutorials rather than notes

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: machine-learning, deep-learning, documentation
- domain: deep-learning, machine-learning, artificial-intelligence, tutorials
- platform: cross-platform
- tags: paper-summaries, research-notes, reading-list, arxiv

## Member repositories
- dennybritz/deeplearning-papernotes (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:48.744843+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 2026-08-29T18:20:59.328547+00:00, confidence not recorded.
  - readme: https://github.com/dennybritz/deeplearning-papernotes (fetched 2026-08-28T04:08:48.744843+00:00, sha f1bc6f22c471)
- Data as of 2026-08-30T08:39:29.467469+00:00.