shusentang/dive-into-dl-pytorch

Analysis updated 2026-06-21

★ 19,386Jupyter NotebookAudience · dataComplexity · 3/5Setup · moderate

Mindmap

mindmap
  root((repo))
    What It Does
      PyTorch code examples
      Textbook adaptation
      Interactive notebooks
    Topics Covered
      Computer vision
      Natural language processing
      Deep learning basics
    Tech Stack
      Python
      PyTorch
      Jupyter Notebooks
    Audience
      Deep learning students
      Chinese language learners

mindmap root((repo)) What It Does PyTorch code examples Textbook adaptation Interactive notebooks Topics Covered Computer vision Natural language processing Deep learning basics Tech Stack Python PyTorch Jupyter Notebooks Audience Deep learning students Chinese language learners

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What do people build with it?

USE CASE 1

Work through the Dive into Deep Learning curriculum with runnable PyTorch code instead of MXNet

USE CASE 2

Study computer vision and NLP techniques with interactive notebooks that mix explanation and live code

USE CASE 3

Use as a practical supplement to the Dive into Deep Learning textbook if you are learning in Chinese

What is it built with?

PythonPyTorchJupyter Notebook

How does it compare?

	shusentang/dive-into-dl-pytorch	fengdu78/lihang-code	qwenlm/qwen3-vl
Stars	19,386	19,578	19,159
Language	Jupyter Notebook	Jupyter Notebook	Jupyter Notebook
Setup difficulty	moderate	easy	moderate
Complexity	3/5	2/5	3/5
Audience	data	developer	developer

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires a Python environment with PyTorch and Jupyter installed, a GPU speeds up training examples but most notebooks run on CPU.

In plain English

This repository is a PyTorch adaptation of the well-known open-source deep learning textbook "Dive into Deep Learning." The original book used MXNet as its coding framework, this project rewrites all of the code examples in PyTorch, a more widely used deep learning library. Topics covered include computer vision and natural language processing, based on the textbook's curriculum. The content is presented as Jupyter Notebooks, interactive documents that mix explanations with runnable code. You would use this if you are learning deep learning and prefer PyTorch over MXNet, or if you want a Chinese-language resource with practical code examples alongside the theoretical concepts.

Copy-paste prompts

Prompt 1

Open the dive-into-dl-pytorch notebook for convolutional neural networks and walk me through training a basic CNN on a small image dataset using PyTorch

Prompt 2

Show me how the attention mechanism notebook in dive-into-dl-pytorch implements scaled dot-product attention in PyTorch and explain each step

Prompt 3

I want to follow the RNN chapter in dive-into-dl-pytorch, give me the setup steps to run the Jupyter notebooks locally and run the first example

Prompt 4

Adapt the sentiment analysis notebook from dive-into-dl-pytorch to classify tweets from my own dataset using the same PyTorch model

Frequently asked questions

What is dive-into-dl-pytorch?

A PyTorch rewrite of all code examples from the Dive into Deep Learning textbook, covering computer vision and NLP topics in interactive Jupyter Notebooks for learners who prefer PyTorch over MXNet.

What language is dive-into-dl-pytorch written in?

Mainly Jupyter Notebook. The stack also includes Python, PyTorch, Jupyter Notebook.

How hard is dive-into-dl-pytorch to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is dive-into-dl-pytorch for?

Mainly data.

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