# kjw0612/awesome-rnn

Recurrent Neural Network - A curated list of resources dedicated to RNN

Repository: https://github.com/kjw0612/awesome-rnn
Canonical: https://ross.abutalabs.com/products/awesome-rnn
License Family: other
Last push: 2022-02-03T05:56:39+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 4097, "days_push": 1672, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6210, forks 1419 (observed 2026-08-28T04:09:40.036603+00:00)

## What it is
A curated list of resources on recurrent neural networks, including code, papers, lectures, books, and application examples across NLP, computer vision, and multimodal tasks. It is an awesome-list style reference rather than software.

## Use cases
- find papers and tutorials on RNNs and LSTMs
- learn about sequence-to-sequence and language modeling resources
- discover RNN code examples and datasets
- explore RNN applications in translation, speech, and image captioning
- find surveys and architecture variants of recurrent networks

## When to choose
- you want a broad curated index of RNN learning materials
- you are researching classic RNN literature and architectures

## When to avoid
- you need actively maintained or up-to-date resources
- you want runnable software rather than a link list
- you need modern transformer-era content

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: documentation
- domain: deep-learning, machine-learning, computer-vision, tutorials
- platform: cross-platform
- tags: awesome-list, recurrent-neural-networks, rnn, lstm, curated-list, papers, tutorials, natural-language-processing

## Member repositories
- kjw0612/awesome-rnn (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:40.036603+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 2026-08-29T17:47:17.954550+00:00, confidence not recorded.
  - readme: https://github.com/kjw0612/awesome-rnn (fetched 2026-08-28T04:09:40.036603+00:00, sha 6ae1cbc71466)
- Data as of 2026-08-30T08:39:29.467469+00:00.