# MLEveryday/practicalAI-cn

AI实战-practicalAI 中文版

Repository: https://github.com/MLEveryday/practicalAI-cn
Canonical: https://ross.abutalabs.com/products/practicalai-cn
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: machine-learning, jupyter-notebook, pytorch, google-colab-notebook, deep-learning
Last push: 2026-04-02T08:05:42+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 75, release rhythm 35, longevity 100
- inputs: {"age_days": 2822, "days_push": 153, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6923, forks 1423 (observed 2026-08-28T04:09:51.416754+00:00)

## What it is
A Chinese translation of the practicalAI project, a collection of Jupyter notebooks teaching practical machine learning and deep learning with PyTorch. All notebooks run in Google Colab in the browser with no setup required, covering basics through advanced topics like CNNs, RNNs, and GANs.

## Use cases
- learn machine learning with pytorch notebooks
- deep learning tutorial in chinese
- run ml notebooks in google colab without setup
- learn object-oriented production ml code
- intro to neural networks with jupyter notebooks
- study cnns rnns and gans hands-on

## When to choose
- you want a free, structured, hands-on ML curriculum runnable in the browser
- you prefer Chinese-language learning materials
- you want to learn PyTorch through practical notebooks

## When to avoid
- you need production-ready ML libraries rather than tutorials
- you need up-to-date coverage of the latest LLM techniques
- you prefer courses in English or other frameworks like TensorFlow

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, data-science
- domain: machine-learning, deep-learning, tutorials, data-science
- platform: python, browser, cross-platform
- tags: jupyter-notebooks, pytorch, google-colab, chinese-translation, hands-on-learning

## Member repositories
- MLEveryday/practicalAI-cn (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:51.416754+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 2026-08-29T17:41:25.455684+00:00, confidence not recorded.
  - readme: https://github.com/MLEveryday/practicalAI-cn (fetched 2026-08-28T04:09:51.416754+00:00, sha 9ea552af8b34)
- Data as of 2026-08-30T08:39:29.467469+00:00.