d2l-pytorch

Why we included this project

Readers working through Dive Into Deep Learning who would rather use PyTorch than the original MXNet examples will find this companion repo easy to follow alongside the book. The notebooks track the same chapters, covering linear algebra and automatic differentiation before moving on to convolutional and recurrent networks, with computer vision and NLP exercises, and the code is reworked into idiomatic PyTorch. That makes it a handy study aid for self-learners and instructors who want to stick with one framework, and running the notebooks lets you experiment as you read. One honest caveat: the maintainers now send readers to the official d2l-ai repository for a complete and maintained PyTorch port, and this project is no longer updated. Treat it as a teaching reference rather than production software, and as a useful secondary look at how the community has interpreted the classic d2l material in PyTorch.