# wepe/MachineLearning

Basic Machine Learning and Deep Learning

Repository: https://github.com/wepe/MachineLearning
Canonical: https://ross.abutalabs.com/products/wepe-machinelearning
Language: Python
License Family: other
Last push: 2024-06-15T23:28:32+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": 4289, "days_push": 809, "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 5752, forks 3205 (observed 2026-08-28T04:09:29.036808+00:00)

## What it is
A collection of educational implementations of common machine learning and deep learning algorithms in Python (with some C++), accompanied by Chinese-language tutorial articles. It covers algorithms like CNN, MLP, softmax regression, PCA, kNN, logistic regression, and SVM applied to datasets such as MNIST.

## Use cases
- learn machine learning algorithms from scratch implementations
- understand how CNN works with annotated code
- study deep learning tutorials with Chinese explanations
- see PCA and kNN implemented in numpy
- learn to use keras and theano for MNIST classification
- find example code for SVM and logistic regression

## When to choose
- you are a beginner learning ML/DL fundamentals through readable implementations
- you prefer Chinese-language explanations alongside code
- you want simple, self-contained algorithm examples rather than production libraries

## When to avoid
- you need a production-ready, maintained ML framework
- you require up-to-date code for modern frameworks like PyTorch or TensorFlow 2.x
- you need software with a license for commercial use

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, data-visualization
- domain: machine-learning, deep-learning, tutorials, data-science
- platform: python, cpp, cross-platform
- tags: educational-code, tutorials, mnist, cnn, svm, pca, knn, logistic-regression, keras, theano, chinese-language

## Member repositories
- wepe/MachineLearning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:29.036808+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 2026-08-29T17:53:18.015325+00:00, confidence not recorded.
  - readme: https://github.com/wepe/MachineLearning (fetched 2026-08-28T04:09:29.036808+00:00, sha 522a047c4a18)
- Data as of 2026-08-30T08:39:29.467469+00:00.