# andrewt3000/DL4NLP

Deep Learning for NLP resources

Repository: https://github.com/andrewt3000/DL4NLP
Canonical: https://ross.abutalabs.com/products/dl4nlp
License Family: other
Last push: 2019-03-29T00:09:29+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": 3974, "days_push": 2715, "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 2180, forks 454 (observed 2026-08-28T04:06:23.217921+00:00)

## What it is
A curated list of deep learning resources for natural language processing, covering courses, papers, and notes on sequence modeling tasks like machine translation, image captioning, and dialog. It includes links to Stanford and Oxford NLP courses, seminal papers on word vectors, and RNN/LSTM materials.

## Use cases
- learn deep learning for NLP from scratch
- find courses on neural networks for text
- understand word2vec and word embeddings
- study sequence modeling for machine translation
- find seminal NLP papers to read
- prepare for NLP research or interviews

## When to choose
- you want a curated starting point for deep learning NLP topics
- you need links to courses, lecture videos, and foundational papers
- you are studying word vectors, RNNs, LSTMs, or sequence-to-sequence models

## When to avoid
- you need runnable code or a software library
- you want up-to-date coverage of transformer-era LLMs (list last updated 2019)
- you need a structured textbook rather than a link collection

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, deep-learning
- domain: machine-learning, deep-learning, tutorials
- platform: -
- tags: awesome-list, curated-resources, word-embeddings, sequence-modeling, papers, courses, natural-language-processing

## Member repositories
- andrewt3000/DL4NLP (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:23.217921+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 2026-08-30T02:48:43.189866+00:00, confidence not recorded.
  - readme: https://github.com/andrewt3000/DL4NLP (fetched 2026-08-28T04:06:23.217921+00:00, sha c1cfae42b6d6)
- Data as of 2026-08-30T08:39:29.467469+00:00.