# ahangchen/GDLnotes

Google Deep Learning Notes（TensorFlow教程）

Repository: https://github.com/ahangchen/GDLnotes
Canonical: https://ross.abutalabs.com/products/gdlnotes
Language: Python
License: NOASSERTION
License Family: other
Topics: tensorflow, machine-learning, deep-learning
Last push: 2022-11-01T05:05:57+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3769, "days_push": 1401, "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 1489, forks 465 (observed 2026-08-28T04:04:52.305374+00:00)

## What it is
A collection of Chinese-language study notes for Google's Udacity deep learning course (ud730), with TensorFlow 1.2-compatible example code. It covers logistic classification, deep neural networks, CNNs, and RNNs, plus NumPy, matplotlib, and sklearn appendices.

## Use cases
- learn deep learning fundamentals from Google's Udacity course
- find TensorFlow tutorial notes in Chinese
- understand logistic classification and stochastic optimization
- study convolutional and recurrent neural networks with examples
- get started with TensorFlow installation and basics

## When to choose
- you want structured notes accompanying the Udacity deep learning course
- you prefer Chinese-language explanations of deep learning concepts
- you are learning TensorFlow 1.x with hands-on examples

## When to avoid
- you need up-to-date TensorFlow 2.x or Keras material
- you want a production library rather than educational notes
- you need English-language resources

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: deep-learning, machine-learning, tutorials
- platform: python
- tags: tensorflow, notes, udacity, chinese, study-notes

## Member repositories
- ahangchen/GDLnotes (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:52.305374+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 2026-08-30T04:33:39.556627+00:00, confidence not recorded.
  - readme: https://github.com/ahangchen/GDLnotes (fetched 2026-08-28T04:04:52.305374+00:00, sha ec62ef710bed)
- Data as of 2026-08-30T08:39:29.467469+00:00.