# MachineLP/Tensorflow-

Tensorflow实战学习笔记、代码、机器学习进阶系列

Repository: https://github.com/MachineLP/Tensorflow-
Canonical: https://ross.abutalabs.com/products/machinelp-tensorflow
Language: Jupyter Notebook
License Family: other
Last push: 2020-09-30T04:03:03+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": 3217, "days_push": 2163, "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 1136, forks 380 (observed 2026-08-28T04:03:43.710461+00:00)

## What it is
A collection of Chinese-language TensorFlow learning notes, code examples, and machine learning study series organized as Jupyter notebooks. It covers TensorFlow in action, machine learning fundamentals, face detection, and TensorFlow API explanations.

## Use cases
- learn tensorflow with practical code examples
- study machine learning fundamentals with annotated code
- find face detection tutorials in tensorflow
- understand tensorflow api usage
- learn deep learning in chinese
- get hands-on tensorflow2 tutorial notebooks

## When to choose
- you prefer learning from annotated notebook code in Chinese
- you want practical TensorFlow examples including face detection
- you are studying machine learning from a hands-on perspective

## When to avoid
- you need up-to-date TensorFlow 2.x content or active maintenance
- you need English-language materials
- you want a production-ready library rather than study notes

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, computer-vision
- domain: machine-learning, deep-learning, tutorials, computer-vision
- platform: python
- tags: tensorflow, jupyter-notebooks, chinese-language, tutorials, face-detection

## Member repositories
- MachineLP/Tensorflow- (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:43.710461+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 2026-08-30T06:36:26.343444+00:00, confidence not recorded.
  - readme: https://github.com/MachineLP/Tensorflow- (fetched 2026-08-28T04:03:43.710461+00:00, sha 40e8f9215397)
- Data as of 2026-08-30T08:39:29.467469+00:00.