# graykode/nlp-tutorial

Natural Language Processing Tutorial for Deep Learning Researchers

Repository: https://github.com/graykode/nlp-tutorial
Canonical: https://ross.abutalabs.com/products/nlp-tutorial
Homepage: https://www.reddit.com/r/MachineLearning/comments/amfinl/project_nlptutoral_repository_who_is_studying/
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: nlp, natural-language-processing, tutorial, pytorch, tensorflow, transformer, attention, paper, bert
Last push: 2024-02-21T13:49:10+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2793, "days_push": 924, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 14924, forks 3937 (observed 2026-08-28T04:11:08.944909+00:00)

## What it is
A Jupyter Notebook-based tutorial series teaching natural language processing with deep learning, implementing classic NLP models (NNLM, Word2Vec, TextCNN, RNN/LSTM, Seq2Seq, Transformer, BERT) in under 100 lines of PyTorch code each. It is aimed at deep learning researchers studying NLP, with Colab notebooks and paper references for each model.

## Use cases
- learn NLP from scratch with PyTorch
- understand how the Transformer attention mechanism works
- implement Word2Vec skip-gram from scratch
- study BERT and Transformer paper implementations
- find minimal reference implementations of classic NLP models
- prepare for NLP research or interviews

## When to choose
- you want concise, readable implementations of classic NLP models
- you are a researcher or student learning NLP fundamentals
- you prefer PyTorch notebooks with linked papers

## When to avoid
- you need production-ready or optimized NLP code
- you want a maintained library with an API rather than educational notebooks
- you need the latest large language model training techniques

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, deep-learning
- domain: deep-learning, tutorials, machine-learning
- platform: python
- tags: pytorch, tensorflow, transformer, attention, bert, jupyter-notebook, word-embeddings, seq2seq, natural-language-processing

## Member repositories
- graykode/nlp-tutorial (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:08.944909+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 2026-08-29T17:06:53.242568+00:00, confidence not recorded.
  - readme: https://github.com/graykode/nlp-tutorial (fetched 2026-08-28T04:11:08.944909+00:00, sha 3015b06cdbff)
- Data as of 2026-08-30T08:39:29.467469+00:00.