# zlotus/notes-LSJU-machine-learning

机器学习笔记

Repository: https://github.com/zlotus/notes-LSJU-machine-learning
Canonical: https://ross.abutalabs.com/products/notes-lsju-machine-learning
Homepage: http://nbviewer.jupyter.org/github/zlotus/notes-LSJU-machine-learning/blob/master/ReadMe.ipynb?flush_cache=true
Language: Jupyter Notebook
License Family: other
Last push: 2017-01-18T10:41:54+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": 3731, "days_push": 3514, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license, no_readme
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1062, forks 387 (observed 2026-08-28T04:03:26.185684+00:00)

## What it is
A collection of Jupyter Notebook study notes (in Chinese) covering Stanford's Machine Learning course, spanning supervised learning, learning theory, unsupervised learning, and reinforcement learning topics. The notes are written and displayed as .ipynb notebooks, best viewed through Nbviewer since GitHub loads them slowly.

## Use cases
- study machine learning fundamentals from Stanford's course in Chinese
- review lecture topics like linear regression, SVMs, and EM algorithms
- find worked notes on PCA, factor analysis, and Gaussian models
- supplement self-study of supervised and unsupervised learning theory
- get an introduction to MATLAB and convex optimization for ML
- review linear algebra and probability theory prerequisites for machine learning

## When to choose
- you want Chinese-language notes following Stanford's ML course lecture by lecture
- you prefer reading concepts in Jupyter Notebook format with math and examples
- you need refresher notes on prerequisites like linear algebra, probability, and convex optimization

## When to avoid
- you need runnable production machine learning code or a library
- you want up-to-date notes reflecting the latest course version
- you require an English-language resource
- you need a maintained project with a license or active development

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science
- domain: machine-learning, education, tutorials
- platform: python, cross-platform
- tags: jupyter-notebook, lecture-notes, stanford-cs229, chinese-language, study-notes, supervised-learning, unsupervised-learning, support-vector-machines, expectation-maximization, principal-component-analysis, markov-decision-processes, linear-regression, logistic-regression, gaussian-mixture-models, convex-optimization, matlab

## Member repositories
- zlotus/notes-LSJU-machine-learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:26.185684+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 2026-08-30T06:56:47.774118+00:00, confidence not recorded.
  - homepage: http://nbviewer.jupyter.org/github/zlotus/notes-LSJU-machine-learning/blob/master/ReadMe.ipynb?flush_cache=true (fetched 2026-08-29T12:58:45.790461+00:00, sha c83a664b9292)
  - site_page: https://nbviewer.org/faq (fetched 2026-08-29T12:58:45.794188+00:00, sha f62b276c6d7d)
- Data as of 2026-08-30T08:39:29.467469+00:00.