# sbrugman/deep-learning-papers

Papers about deep learning ordered by task, date. Current state-of-the-art papers are labelled.

Repository: https://github.com/sbrugman/deep-learning-papers
Canonical: https://ross.abutalabs.com/products/deep-learning-papers
License Family: other
Topics: deep-learning, neural-networks, papers, arxiv, deep-learning-papers, machine-learning, science
Archived: true
Last push: 2019-12-21T20:49:39+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3595, "days_push": 2447, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3182, forks 403 (observed 2026-08-28T04:07:47.894832+00:00)

## What it is
A curated reading list of deep learning research papers organized by task (code, text, vision, audio) and ordered by date, with state-of-the-art papers labeled. It is a static reference document hosted on GitHub, not executable software.

## Use cases
- find state-of-the-art deep learning papers by task
- reading list for learning deep learning
- find papers on image segmentation or object recognition
- find NLP papers like summarization and question answering
- catch up on recent arxiv deep learning research
- find seminal papers for a literature review

## When to choose
- you want a task-organized index of deep learning papers with links
- you need a quick reference for SOTA papers per topic
- you are building a self-study curriculum in deep learning

## When to avoid
- you need runnable code or implementations
- you need papers newer than the last update in 2019
- you need peer-reviewed summaries rather than raw paper links

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, nlp, computer-vision, documentation
- domain: deep-learning, machine-learning, computer-vision, tutorials, awesome-lists
- platform: cross-platform
- tags: papers, arxiv, reading-list, state-of-the-art, curated-list, natural-language-processing

## Member repositories
- sbrugman/deep-learning-papers (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:47.894832+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 2026-08-30T07:25:03.973519+00:00, confidence not recorded.
  - readme: https://github.com/sbrugman/deep-learning-papers (fetched 2026-08-28T04:07:47.894832+00:00, sha 65bb856cf19d)
- Data as of 2026-08-30T08:39:29.467469+00:00.