# jiqizhixin/ML-Tutorial-Experiment

Coding the Machine Learning Tutorial for Learning to Learn

Repository: https://github.com/jiqizhixin/ML-Tutorial-Experiment
Canonical: https://ross.abutalabs.com/products/ml-tutorial-experiment
Language: Jupyter Notebook
License Family: other
Last push: 2019-02-25T01:11:41+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": 3292, "days_push": 2747, "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 2489, forks 720 (observed 2026-08-28T04:06:55.756548+00:00)

## What it is
A collection of Jupyter Notebook tutorials from Jiqizhixin (Synced) that implement machine learning models from scratch, including CNNs, GANs, and CapsNets using TensorFlow and Keras. It accompanies a series of Chinese-language 'Learning to Learn' tutorial articles.

## Use cases
- learn to build a CNN from scratch in TensorFlow
- understand and implement GANs with theory derivations
- study CapsNet architecture with code
- practice deep learning with LeNet-5 and Keras examples
- follow along with machine learning tutorial articles

## When to choose
- you want hands-on notebook-based tutorials for classic deep learning models
- you prefer explanations paired with runnable TensorFlow/Keras code
- you are learning ML fundamentals from scratch

## When to avoid
- you need production-ready or maintained ML code
- you require a licensed library for a project
- you need up-to-date TensorFlow 2.x or PyTorch examples

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: machine-learning, deep-learning
- domain: machine-learning, deep-learning, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, tensorflow, keras, cnn, gan, capsnet, chinese-language, educational

## Member repositories
- jiqizhixin/ML-Tutorial-Experiment (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:55.756548+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 2026-08-30T02:27:50.613850+00:00, confidence not recorded.
  - readme: https://github.com/jiqizhixin/ML-Tutorial-Experiment (fetched 2026-08-28T04:06:55.756548+00:00, sha f8c6fc4434a4)
- Data as of 2026-08-30T08:39:29.467469+00:00.