This repository provides generated notebook slides for PyTorch-based deep learning concepts, aimed at students and practitioners learning deep learning. It offers a structured, visual presentation of core topics, complementing the d2l-ai/d2l-zh book.

How It Works

The slides are generated from Jupyter notebooks, likely using a tool like RISE (Reactivity Slideshow Extension). This approach allows for interactive presentations directly within a web browser, enabling code execution and visualization of deep learning concepts. The organization follows a chapter-based structure, covering foundational elements to advanced architectures.

Quick Start & Requirements

• To open locally, install the RISE extension for Jupyter Notebook.
• Alternatively, preview slides via nbviewer.
• Requires a Jupyter Notebook environment.

Highlighted Details

• Comprehensive coverage from preliminaries (NumPy, Pandas, Linear Algebra) to advanced topics like Transformers, Attention Mechanisms, and modern CNNs (ResNet, DenseNet).
• Includes practical applications in computer vision (object detection, segmentation) and natural language processing (BERT, machine translation).
• Notebooks are structured for both "scratch" implementations and concise, library-based approaches.

Maintenance & Community

• Part of the Deep Learning with PyTorch (d2l.ai) ecosystem, indicating a connection to a broader educational initiative.
• No specific community links (Discord, Slack) or contributor details are provided in the README.

Licensing & Compatibility

• The repository itself is not explicitly licensed in the README. However, it is associated with the d2l.ai project, which typically uses permissive licenses like Apache 2.0 for its code and content.

Limitations & Caveats

• The README focuses solely on presentation and does not include the underlying code or data for the notebooks themselves, which would be found in the main d2l-zh repository.
• No information on the generation process or specific versioning of PyTorch or other dependencies used.