What is d2l-zh-pytorch-slides?

This repository is a collection of presentation slides for learning deep learning, using PyTorch (a popular programming tool for AI). It turns interactive coding notebooks into slide decks, so you can learn concepts like linear regression, convolutional neural networks, and transformers in a visual, step-by-step format. The content covers a broad curriculum. It starts with fundamentals like linear algebra and calculus, then moves into building basic neural networks from scratch and using concise library code. Later chapters cover image classification, object detection, recurrent neural networks for text, attention mechanisms, and even practical projects like predicting house prices or classifying images in Kaggle competitions. Each topic is a separate notebook file that doubles as a slide presentation. You would use this if you are a student, instructor, or self-learner trying to understand deep learning with PyTorch. Teachers can use the slides directly in lectures, while learners can click through them to see both the theory and runnable code side by side. For example, if you want to understand how a ResNet architecture works or how to fine-tune a model for computer vision, you can open that specific slide deck and follow along. To view the slides, you can either preview them online through nbviewer or open them locally. For the local experience, the project recommends installing a tool called RISE, which turns Jupyter notebooks into live presentations. The README doesn't go into detail about installation steps beyond that suggestion. The project is notable for its sheer breadth, it spans from absolute basics to advanced topics like BERT and multi-head attention, all in slide format. The content appears to be in Chinese, given the original description, making it a resource primarily aimed at Chinese-speaking learners, though the code and mathematical notation are universally accessible.