This repository is a comprehensive, continuously updated collection of resources for deep learning, artificial intelligence, and machine learning. It targets engineers, researchers, and students seeking curated materials, tutorials, code examples, and foundational knowledge in these fields, aiming to accelerate learning and project development.

How It Works

The repository functions as a curated knowledge base, organizing a vast array of links and information into logical categories. It covers foundational Python programming, various deep learning frameworks (TensorFlow, Keras, Caffe, PyTorch), essential mathematical concepts, industry news, and practical application examples like web scraping and image recognition. The structure facilitates a guided learning path from basic concepts to advanced research papers and tools.

Quick Start & Requirements

• Installation: Primarily relies on accessing linked external resources and cloning GitHub repositories.
• Prerequisites: Python, Anaconda (recommended for environment management), Git. Specific deep learning frameworks may require CUDA-enabled GPUs for optimal performance.
• Resources: Extensive links to online courses (Coursera, edX), documentation, research papers (arXiv), datasets (ImageNet, COCO), and popular libraries.

Highlighted Details

• Extensive coverage of major deep learning frameworks including TensorFlow, Keras, Caffe, and MXNet.
• Curated lists of top-cited research papers and learning roadmaps.
• Practical examples and tutorials for web scraping, image recognition, and NLP tasks.
• Links to industry news, policy updates, and influential AI companies.

Maintenance & Community

The project is maintained by Robinwho, with a note indicating potential delays in updates due to work commitments. The author encourages community engagement via a public WeChat account ("久哥传奇").

Licensing & Compatibility

The repository itself is a collection of links; the licensing of the linked projects varies widely, with many popular deep learning libraries (TensorFlow, Keras, PyTorch) being open-source (e.g., Apache 2.0, MIT). Users must consult the individual licenses of linked resources for compatibility and usage terms.

Limitations & Caveats

The repository is a curated list of external resources, not a self-contained software project. The quality and maintenance of linked resources are external to this repository. The last significant update mentioned was April 2019, with a previous update in January 2018, suggesting potential staleness for rapidly evolving fields.