# yuanxiaosc/Machine-Learning-Book

《机器学习宝典》包含：谷歌机器学习速成课程（招式）+机器学习术语表（口诀）+机器学习规则（心得）+机器学习中的常识性问题 （内功）。该资源适用于机器学习、深度学习研究人员和爱好者参考！

Repository: https://github.com/yuanxiaosc/Machine-Learning-Book
Canonical: https://ross.abutalabs.com/products/yuanxiaosc-machine-learning-book
Homepage: https://yuanxiaosc.github.io/2019/08/16/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B8%AD%E7%9A%84%E5%B8%B8%E8%AF%86%E6%80%A7%E9%97%AE%E9%A2%98/
Language: Jupyter Notebook
License Family: other
Topics: machine-learning, deep-learning, course, google, tensorflow-tutorials
Last push: 2020-06-07T11:37:29+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": 2656, "days_push": 2278, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1160, forks 277 (observed 2026-08-28T04:03:49.046723+00:00)

## What it is
A Chinese-language curated collection of machine learning learning materials, including the Google Machine Learning Crash Course, a terminology glossary, ML best-practice rules, illustrated concept summaries, and a list of common ML questions. It is aimed at machine learning and deep learning researchers and hobbyists as a reference for self-study.

## Use cases
- learn machine learning fundamentals from scratch
- study the Google Machine Learning Crash Course with TensorFlow code examples
- review machine learning terminology and definitions
- prepare for machine learning interviews with common conceptual questions
- understand bias-variance tradeoff and evaluation metrics like ROC and AUC
- find illustrated explanations of machine learning concepts

## When to choose
- you want a free, curated Chinese-language ML study path from beginner to intermediate
- you prefer PDF/illustrated materials and structured course notes over scattered tutorials
- you want to self-test with common ML conceptual questions and evaluation metric summaries

## When to avoid
- you need up-to-date content or active community support, since the repo has not been updated since 2020
- you need English-language materials only
- you need a runnable software library or production codebase rather than study documents

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, documentation
- domain: machine-learning, deep-learning, tutorials, education
- platform: cross-platform
- tags: chinese-language, study-notes, google-machine-learning-crash-course, pdf-resources, jupyter-notebook, glossary

## Member repositories
- yuanxiaosc/Machine-Learning-Book (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:49.046723+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 2026-08-30T06:31:50.575281+00:00, confidence not recorded.
  - readme: https://github.com/yuanxiaosc/Machine-Learning-Book (fetched 2026-08-28T04:03:49.046723+00:00, sha 56f3f506ea9b)
  - homepage: https://yuanxiaosc.github.io/2019/08/16/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B8%AD%E7%9A%84%E5%B8%B8%E8%AF%86%E6%80%A7%E9%97%AE%E9%A2%98/ (fetched 2026-08-29T12:36:28.487347+00:00, sha 6efa55784bda)
- Data as of 2026-08-30T08:39:29.467469+00:00.