graykode/nlp-tutorial resource

Health v2 · maintenance only

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14924 stars · 3937 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

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

learning-resource · maturity maintenance

nlp machine-learning deep-learning deep-learning tutorials machine-learning python pytorch tensorflow transformer attention bert jupyter-notebook word-embeddings seq2seq natural-language-processing

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