Dive-into-DL-PyTorch

Why we included this project

Learning deep learning from the original Dive into Deep Learning book means working through MXNet code, which is a hurdle if you're a PyTorch user. This project removes that hurdle by porting every chapter's code to PyTorch, so you can follow the same explanations and run the notebooks as you go. It walks from linear regression and softmax through CNNs, RNNs, and into practical territory like optimization, multi-GPU training, object detection, and word embeddings. The chapters pair readable markdown with executable notebooks, and the whole thing is also available as a browsable site, so you can read online or clone it locally to experiment. If you have basic Python and math, you can work through it on your own without a formal course.