# Miraclelucy/dive_into_deep_learning

✔️李沐 【动手学深度学习】课程学习笔记：使用pycharm编程，基于pytorch框架实现。

Repository: https://github.com/Miraclelucy/dive_into_deep_learning
Canonical: https://ross.abutalabs.com/products/dive_into_deep_learning
Language: Python
License Family: other
Last push: 2023-09-11T07:49:21+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1986, "days_push": 1087, "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 3074, forks 504 (observed 2026-08-28T04:07:41.697760+00:00)

## What it is
A collection of Python study notes for Mu Li's 'Dive into Deep Learning' course, rewritten from the original Jupyter notebooks into standalone .py files using PyCharm and PyTorch. It covers fundamentals through CNNs with runnable implementations of linear regression, softmax regression, MLPs, and convolutional networks.

## Use cases
- learn deep learning with pytorch following the d2l course
- find pytorch implementations of linear regression from scratch
- study softmax regression and mlp examples in python
- understand autograd and linear algebra basics for deep learning
- get runnable py scripts instead of jupyter notebooks for d2l
- prepare for kaggle house price prediction with deep learning

## When to choose
- you are following Mu Li's Dive into Deep Learning course and prefer .py files over notebooks
- you want from-scratch PyTorch implementations of classic neural network concepts
- you read Chinese and want course-aligned study notes

## When to avoid
- you need a production deep learning framework or library
- you want English-language materials or the official d2l.ai content
- you need maintained, licensed code for a real project

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, machine-learning, developer-tools
- domain: deep-learning, machine-learning, tutorials, education
- platform: python
- tags: pytorch, d2l, course-notes, study-notes, chinese, linear-regression, cnn, mlp

## Member repositories
- Miraclelucy/dive_into_deep_learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:41.697760+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 2026-08-30T07:27:56.100787+00:00, confidence not recorded.
  - readme: https://github.com/Miraclelucy/dive_into_deep_learning (fetched 2026-08-28T04:07:41.697760+00:00, sha 0dd849d10a41)
- Data as of 2026-08-30T08:39:29.467469+00:00.