zergtant/pytorch-handbook

This repository is a Chinese-language open-source handbook for learning PyTorch, a popular Python library used for building and training deep learning models (software that learns patterns from data, such as image recognition or language processing). The goal, as described in the README, is to help people who want to use PyTorch quickly get up and running, with all included tutorials tested to confirm they run successfully. The content is organized as a structured course across several chapters. It begins with an introduction to PyTorch and environment setup, then covers fundamental building blocks like tensors (the data structures that hold numbers in deep learning), automatic differentiation (how a model learns by computing gradients, a mathematical technique for adjusting itself to reduce errors), and neural network modules. Later chapters move into specific network architectures: convolutional neural networks (CNNs, used commonly for image tasks) and recurrent neural networks (RNNs, used for sequences like time series or text). The practical chapters include hands-on exercises such as classifying handwritten digits and predicting values from a wave pattern. More advanced topics cover fine-tuning existing models, visualizing training progress, and training across multiple GPUs simultaneously. All tutorials are delivered as Jupyter notebooks, which are interactive documents that combine explanatory text and runnable code in one place. You would use this repository if you want to learn deep learning using PyTorch and prefer Chinese-language materials with practical, runnable examples. It is licensed for non-commercial use under a Creative Commons license.