# ljpzzz/machinelearning

My blogs and code for machine learning. http://cnblogs.com/pinard

Repository: https://github.com/ljpzzz/machinelearning
Canonical: https://ross.abutalabs.com/products/ljpzzz-machinelearning
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: machinelearning, algorithms, scikit-learn, reinforcementlearning
Last push: 2024-02-16T15:26:27+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": 3765, "days_push": 929, "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 8705, forks 3684 (observed 2026-08-28T04:10:24.312612+00:00)

## What it is
A collection of Jupyter Notebook code accompanying Liu Jianping (Pinard)'s widely-read Chinese machine learning blog on cnblogs. It organizes blog code into topics covering regression, classification, clustering, dimensionality reduction, ensemble learning, deep learning, NLP, and reinforcement learning.

## Use cases
- learn machine learning algorithms with runnable python code
- study reinforcement learning from Q-learning to DDPG with examples
- find companion code for Pinard's blog tutorials
- understand scikit-learn algorithm implementations
- learn deep learning with TensorFlow notebooks
- study NLP algorithms like word2vec and LDA with code

## When to choose
- you want structured, tutorial-style code paired with detailed blog explanations
- you are learning classical ML, deep learning, or RL fundamentals in Python
- you prefer Chinese-language learning materials

## When to avoid
- you need a production-ready ML library or framework
- you need actively maintained code compatible with the latest TensorFlow or Python versions
- you cannot read Chinese since the blog posts are in Chinese

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, reinforcement-learning, nlp
- domain: machine-learning, deep-learning, tutorials, data-science
- platform: python
- tags: jupyter-notebooks, tutorials, blog-code, scikit-learn, chinese-language, algorithms, natural-language-processing

## Member repositories
- ljpzzz/machinelearning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:24.312612+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 2026-08-29T17:25:33.222722+00:00, confidence not recorded.
  - readme: https://github.com/ljpzzz/machinelearning (fetched 2026-08-28T04:10:24.312612+00:00, sha d1ec74938737)
- Data as of 2026-08-30T08:39:29.467469+00:00.