# oxford-cs-deepnlp-2017/lectures

Oxford Deep NLP 2017 course

Repository: https://github.com/oxford-cs-deepnlp-2017/lectures
Canonical: https://ross.abutalabs.com/products/lectures
License Family: other
Topics: deep-learning, machine-learning, natural-language-processing, nlp, oxford
Last push: 2023-07-02T22:46:23+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": 3495, "days_push": 1158, "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 15853, forks 3544 (observed 2026-08-28T04:11:14.265127+00:00)

## What it is
Repository of lecture slides and course materials for the Oxford Deep NLP 2017 course, delivered with DeepMind. It covers neural network approaches to language modelling, machine translation, speech recognition, and question answering.

## Use cases
- learn deep learning for NLP
- study neural language models
- find lecture slides on machine translation
- self-study recurrent neural networks for text
- prepare for a course on natural language processing

## When to choose
- you want structured university-level course material on deep NLP
- you are learning RNNs, seq2seq, and language modelling from slides

## When to avoid
- you need up-to-date content covering transformers and LLMs
- you need runnable software rather than lecture slides

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, deep-learning, machine-learning
- domain: deep-learning, education, tutorials
- platform: python
- tags: lecture-slides, course-materials, oxford, deepmind, rnn, natural-language-processing

## Member repositories
- oxford-cs-deepnlp-2017/lectures (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:14.265127+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 2026-08-29T17:05:21.039378+00:00, confidence not recorded.
  - readme: https://github.com/oxford-cs-deepnlp-2017/lectures (fetched 2026-08-28T04:11:14.265127+00:00, sha 95e4d861f2b6)
- Data as of 2026-08-30T08:39:29.467469+00:00.