# zhangxiann/PyTorch_Practice

这是我学习 PyTorch 的笔记对应的代码，点击查看 PyTorch 笔记在线电子书

Repository: https://github.com/zhangxiann/PyTorch_Practice
Canonical: https://ross.abutalabs.com/products/pytorch_practice
Homepage: http://pytorch.zhangxiann.com/
Language: Python
License: GPL-3.0
License Family: copyleft
Last push: 2020-12-05T10:14:19+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": 2302, "days_push": 2097, "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 1397, forks 293 (observed 2026-08-28T04:04:36.717745+00:00)

## What it is
A collection of PyTorch learning notes with 25 chapters organized into an 8-week study plan, accompanied by runnable Python code examples and an online GitBook ebook. It covers tensors, autograd, data loading, model building and training, visualization with TensorBoard, regularization, and applications like ResNet, object detection, GANs, and RNNs.

## Use cases
- learn pytorch from scratch with a structured plan
- understand tensors and autograd in pytorch
- learn dataloader and transforms for image preprocessing
- understand loss functions and optimizers in pytorch
- learn to visualize training with tensorboard
- study resnet source code and finetuning
- learn to implement rnn manually in pytorch

## When to choose
- you want a structured, week-by-week PyTorch curriculum with runnable code
- you already know basic machine/deep learning concepts and want PyTorch specifics
- you prefer Chinese-language tutorials with an accompanying ebook

## When to avoid
- you need up-to-date coverage of the latest PyTorch APIs (notes last updated ~2020)
- you are a complete beginner to machine learning
- you need production-grade reference implementations rather than educational examples

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, developer-tools
- domain: deep-learning, machine-learning, tutorials, computer-vision
- platform: python, cross-platform
- tags: pytorch, tutorial, notes, ebook, chinese, deep-learning-course, tensorboard, gan, rnn

## Member repositories
- zhangxiann/PyTorch_Practice (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:36.717745+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 2026-08-30T04:39:17.656129+00:00, confidence not recorded.
  - readme: https://github.com/zhangxiann/PyTorch_Practice (fetched 2026-08-28T04:04:36.717745+00:00, sha 817eeaa9531b)
  - homepage: http://pytorch.zhangxiann.com/ (fetched 2026-08-29T11:53:44.119797+00:00, sha 3eaefa3b6531)
- Data as of 2026-08-30T08:39:29.467469+00:00.