# zhulei227/ML_Notes

机器学习算法的公式推导以及numpy实现

Repository: https://github.com/zhulei227/ML_Notes
Canonical: https://ross.abutalabs.com/products/ml_notes
Language: Jupyter Notebook
License Family: other
Last push: 2023-05-02T23:34:18+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2384, "days_push": 1219, "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 2128, forks 481 (observed 2026-08-28T04:06:16.996635+00:00)

## What it is
A collection of Jupyter Notebook tutorials covering mainstream machine learning algorithms with mathematical formula derivations, problem analysis, and from-scratch implementations in numpy. Topics include linear models, SVM, decision trees, ensemble learning, and optimization methods like quasi-Newton algorithms.

## Use cases
- learn machine learning algorithm derivations from scratch
- understand how SVM and SMO work mathematically
- implement decision trees like ID3, C4.5, and CART in numpy
- study boosting and ensemble learning internals
- see numpy implementations of linear and logistic regression
- learn quasi-Newton optimization methods like DFP and BFGS

## When to choose
- you want to deeply understand the math behind ML algorithms rather than just call sklearn
- you prefer learning through runnable notebooks with derivations and code side by side
- you are comfortable reading Chinese-language technical material

## When to avoid
- you need production-ready, well-tested ML libraries
- you require English documentation
- you need deep learning or modern transformer-based content

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, math, data-science
- domain: machine-learning, tutorials, data-science
- platform: python
- tags: jupyter-notebooks, numpy-implementations, formula-derivations, educational, chinese-language, svm, decision-trees, ensemble-learning, algorithms

## Member repositories
- zhulei227/ML_Notes (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:16.996635+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 2026-08-30T02:52:27.687401+00:00, confidence not recorded.
  - readme: https://github.com/zhulei227/ML_Notes (fetched 2026-08-28T04:06:16.996635+00:00, sha 842e39cdedb2)
- Data as of 2026-08-30T08:39:29.467469+00:00.