#4747th of 55 in Learning and cookbooks
ml-nlp
Chinese-language ML and NLP interview study notes with code examples
- Stars
- 17.8k
- License
- no license
- Last commit
- Jan 2026
- Language
- Jupyter Notebook
Missed the latest GitHub check; these facts may be older.
Overview
A set of Markdown chapters, with Jupyter notebook code, covering topics commonly asked in machine learning and NLP interviews. Chapters run from linear regression, tree ensembles (GBDT, XGBoost, LightGBM) and SVM through CNN/RNN/LSTM, word embeddings, seq2seq, Transformer, BERT and XLNet. Two project chapters (recommendation system, customer service) exist; knowledge graph and review analysis are listed but empty.
Who it is for: Chinese-reading candidates preparing for ML and NLP engineer interviews
Strengths
- Covers classical ML, deep learning and NLP in one chapter index
- Chapters end with practical code examples, per the README
- Includes Transformer, BERT and XLNet chapters alongside older RNN-era models
- Each chapter is a standalone Markdown page, easy to skim for review
Weaknesses
- Written in Chinese only; no English version noted
- No license file detected, so reuse terms are unclear
- Nothing on modern LLMs, fine-tuning or serving
- Knowledge graph and review analysis chapters are listed but empty
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