# jacobeisenstein/gt-nlp-class

Course materials for Georgia Tech CS 4650 and 7650, "Natural Language"

Repository: https://github.com/jacobeisenstein/gt-nlp-class
Canonical: https://ross.abutalabs.com/products/gt-nlp-class
Language: TeX
License Family: other
Last push: 2023-01-31T05:37:16+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": 4934, "days_push": 1310, "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 5081, forks 1093 (observed 2026-08-28T04:09:09.573535+00:00)

## What it is
Course materials for Georgia Tech CS 4650/7650 'Natural Language', including extensive TeX lecture notes covering data-driven NLP from bag-of-words models to structural representations. It serves as a free textbook-style resource on natural language processing and the machine learning techniques behind it.

## Use cases
- learn natural language processing from scratch
- find free NLP lecture notes or textbook
- study machine learning for text
- prepare for a university NLP course
- understand linguistic concepts behind language technology
- get course materials for teaching NLP

## When to choose
- you want a rigorous, theory-oriented introduction to NLP
- you need free, well-written lecture notes in TeX/PDF form
- you are an instructor designing an NLP syllabus

## When to avoid
- you want runnable code or a software library
- you need up-to-date coverage of modern LLMs
- you need a license permitting redistribution

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, documentation
- domain: education, machine-learning, tutorials
- platform: python
- tags: course-materials, lecture-notes, tex, university-course, textbook, natural-language-processing

## Member repositories
- jacobeisenstein/gt-nlp-class (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:09.573535+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 2026-08-29T18:02:34.544411+00:00, confidence not recorded.
  - readme: https://github.com/jacobeisenstein/gt-nlp-class (fetched 2026-08-28T04:09:09.573535+00:00, sha 3cd07e933b84)
- Data as of 2026-08-30T08:39:29.467469+00:00.