# datawhalechina/statistical-learning-method-solutions-manual

机器学习方法习题解答，在线阅读地址：https://datawhalechina.github.io/statistical-learning-method-solutions-manual

Repository: https://github.com/datawhalechina/statistical-learning-method-solutions-manual
Canonical: https://ross.abutalabs.com/products/statistical-learning-method-solutions-manual
Homepage: https://datawhalechina.github.io/statistical-learning-method-solutions-manual
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Topics: machine-learning, statistical-learning-method
Last push: 2026-05-08T04:36:38+00:00

## Health v2 (maintenance only)
Score: 69/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 81, release rhythm 35, longevity 100
- inputs: {"age_days": 2405, "days_push": 117, "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 2080, forks 250 (observed 2026-08-28T04:06:11.579169+00:00)

## What it is
An open-source solutions manual for the exercise problems in Li Hang's textbooks 'Statistical Learning Methods' and 'Machine Learning Methods', published as an online VitePress book with Jupyter Notebook sources. It provides detailed derivations, errata, and Python/PyTorch implementations of algorithms like perceptron, decision trees, SVM, EM, and Transformer.

## Use cases
- solutions to exercises in statistical learning methods textbook
- learn machine learning fundamentals with worked examples
- python implementations of svm decision tree em algorithm
- study guide for machine learning exams and interviews
- understand math derivations of classic ml algorithms
- hands-on pytorch examples of cnn rnn transformer

## When to choose
- you are studying Li Hang's textbooks and want step-by-step exercise solutions
- you want runnable Python/PyTorch code alongside theory derivations
- you are a beginner preparing for exams or interviews on ML fundamentals

## When to avoid
- you need a production machine-learning library or framework
- you want a complete, polished resource - this is an alpha, partially finished build
- you need coverage of topics beyond the textbook's scope

## Facets
- artifact type: learning-resource
- maturity: experimental
- function: machine-learning, data-science, documentation
- domain: machine-learning, deep-learning, tutorials, education
- platform: python, cross-platform
- tags: jupyter-notebook, exercise-solutions, textbook-companion, statistical-learning, pytorch, vitepress, chinese, education, web

## Member repositories
- datawhalechina/statistical-learning-method-solutions-manual (main) score 69

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:11.579169+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 2026-08-30T02:56:02.363566+00:00, confidence not recorded.
  - readme: https://github.com/datawhalechina/statistical-learning-method-solutions-manual (fetched 2026-08-28T04:06:11.579169+00:00, sha 8b98541da0d1)
  - homepage: https://datawhalechina.github.io/statistical-learning-method-solutions-manual (fetched 2026-08-29T10:36:03.855896+00:00, sha 5b2bfd32c382)
- Data as of 2026-08-30T08:39:29.467469+00:00.