This repository provides comprehensive Markdown notes and Jupyter code examples for Mu Li's "Dive into Deep Learning" course. It's designed for students and practitioners seeking to understand and implement deep learning concepts, offering a structured learning path that aligns with the course's 73 video lectures. The project aims to facilitate self-study and practical application of deep learning models.

How It Works

The project meticulously maps Markdown notes and executable Jupyter notebooks to each of the 73 course video lectures. Each Jupyter notebook contains detailed Chinese comments, enabling users to quickly grasp and experiment with the implemented deep learning models, from foundational concepts like linear networks and MLPs to advanced architectures such as ResNet, LSTM, and BERT.

Quick Start & Requirements

• Installation: Clone the repository and navigate to the code directory.
• Prerequisites: Python 3.x, PyTorch. Specific dependencies are managed within the Jupyter notebooks.
• Resources: Access to course videos (Bilibili), textbooks (online), and the official course website is recommended.

Highlighted Details

• Markdown notes are directly linked to corresponding video lectures for synchronized learning.
• Jupyter notebooks feature detailed Chinese comments for enhanced code comprehension and practical implementation.
• Covers a broad spectrum of deep learning topics, including CNNs, RNNs, attention mechanisms, optimization, computer vision, and NLP.
• Includes practical examples and Kaggle competition code snippets.

Maintenance & Community

The project is a community-driven effort, with contributions acknowledged. Further discussion and support can be found on the d2l.ai and PyTorch forums.

Licensing & Compatibility

The repository's licensing is not explicitly stated in the provided README. Users should verify compatibility for commercial or closed-source integration.

Limitations & Caveats

The README does not specify licensing details, which may impact commercial use. While comprehensive, the notes and code are supplementary to the official course materials and may not cover every nuance or the latest updates.