This repository is a curated collection of online deep learning resources, designed to guide learners from foundational concepts to advanced applications. It targets individuals new to deep learning, developers seeking practical examples, and researchers tracking the latest trends, offering a structured path to understanding and applying AI.

How It Works

The collection is organized into thematic sections: "Playground" for interactive, browser-based demos; "Online Courses" for structured learning; "Practical Tools" for development aids; "Other Materials" for supplementary texts and articles; "Quality Articles" for conceptual clarity; "Classic Papers" for foundational research; and "Other Collections" for broader resource discovery. This multi-faceted approach allows users to engage with deep learning through hands-on experimentation, formal education, and practical application.

Quick Start & Requirements

• Installation: No direct installation required; resources are primarily web-based links to interactive demos, online courses, and GitHub repositories.
• Prerequisites: Basic Python programming knowledge is recommended for many resources. Some courses may require familiarity with NumPy, Keras, or TensorFlow. Access to a web browser is essential.
• Resources: Many resources are free. Paid courses (e.g., Coursera) have subscription models. Some demos may require specific browser features or plugins.
• Links:
  • Interactive Demos: https://github.com/leemengtw/deep-learning-resources#遊玩空間
  • Online Courses: https://github.com/leemengtw/deep-learning-resources#線上課程
  • Practical Tools: https://github.com/leemengtw/deep-learning-resources#實用工具

Highlighted Details

• Extensive coverage of interactive demos (e.g., Deep Playground, GAN Lab, Talk to Transformer) for intuitive understanding.
• Curated list of high-quality online courses from renowned institutions and instructors (e.g., Andrew Ng, Li Hongyi, fast.ai).
• Practical tools like Colaboratory, TensorBoard, and Papers with Code are highlighted for development and research.
• Comprehensive lists of classic NLP and Computer Vision papers with brief explanations.

Maintenance & Community

The repository is actively maintained, with a clear "To be Updated" section indicating future additions. Contributions are welcomed via GitHub Issues.

Licensing & Compatibility

The repository itself is a collection of links and does not have a specific license. Individual linked resources may have their own licenses. Compatibility for commercial use depends on the licenses of the linked external resources.

Limitations & Caveats

The repository is a pointer to external resources; the quality and availability of these resources are subject to their original creators. Some older links or demos might become outdated or inaccessible over time. The "Classic Papers" section is marked as "WIP" for brief explanations.