This repository provides PyTorch implementations for the "Dive into Deep Learning" textbook, targeting individuals interested in learning deep learning with PyTorch. It offers a comprehensive set of Jupyter notebooks and markdown documentation, enabling users to understand and experiment with various deep learning concepts and models.

How It Works

The project translates the original MXNet code examples from the "Dive into Deep Learning" textbook into PyTorch. It organizes code in Jupyter notebooks and presents the book's content in markdown format, which is then deployed as a website using docsify. This approach allows for a direct, hands-on learning experience with PyTorch implementations of core deep learning algorithms and architectures.

Quick Start & Requirements

• View Documentation: Visit the project's GitHub Pages website for rendered markdown documentation with LaTeX support.
• Run Code Locally:
  • Clone the repository: git clone https://github.com/ShusenTang/Dive-into-DL-PyTorch.git
  • Navigate to the directory: cd Dive-into-DL-PyTorch
  • Install dependencies (e.g., PyTorch, Jupyter).
  • Run Jupyter notebooks from the code directory.
• Local Documentation Server:
  • Install docsify-cli: npm i docsify-cli -g
  • Serve docs locally: docsify serve docs (access at http://localhost:3000)
• Docker:
  • Build image: docker build -t d2dl .
  • Run container: docker run -dp 3000:3000 d2dl (access at http://localhost:3000/#/)

Highlighted Details

• Comprehensive coverage of deep learning topics, from fundamentals to advanced architectures like CNNs, RNNs, and attention mechanisms.
• Includes practical examples and Kaggle competition implementations for image classification, object detection, and NLP tasks.
• Provides both "from scratch" and concise PyTorch implementations for many algorithms.
• Supports GPU acceleration for computations.

Maintenance & Community

The project is a community effort to adapt the popular "Dive into Deep Learning" textbook to PyTorch. Contributions and issues are welcomed. The original book is maintained by Aston Zhang, Mu Li, Zachary C. Lipton, Alexander J. Smola, and other contributors.

Licensing & Compatibility

The repository's licensing is not explicitly stated in the README. The original "Dive into Deep Learning" book is available under a Creative Commons license, but the specific license for this PyTorch code adaptation should be verified. Compatibility for commercial use or closed-source linking depends on the project's license.

Limitations & Caveats

The README notes that the Chinese and English versions of the original book have differences, and this PyTorch version is based on the English version's structure. Some sections, particularly in computer vision (9.6, 9.9, 9.11), are marked with [ ], indicating they may be incomplete or still under development.