This repository provides a structured roadmap for learning the mathematical foundations of deep learning. It is designed for students, researchers, and practitioners seeking a comprehensive understanding of the underlying principles, offering curated links to resources for each topic.

How It Works

The roadmap systematically breaks down deep learning mathematics into key areas: Linear Algebra, Probability and Statistics, Calculus, Numerical Computation, Machine Learning Fundamentals, Deep Learning Basics, and Advanced Deep Learning Topics. For each concept, it lists multiple learning resources, including tutorials, courses, books, and videos, facilitating a self-paced learning journey.

Quick Start & Requirements

This is a curated list of learning resources, not a software package. No installation or execution is required. Users can directly navigate the README to access the provided links.

Highlighted Details

• Comprehensive coverage from foundational math to advanced deep learning topics.
• Extensive curation of diverse learning resources (tutorials, courses, books, videos).
• Organized structure for a clear learning path.
• Includes links to seminal works like the "Deep Learning" book by Goodfellow et al.

Maintenance & Community

The project appears to be a personal compilation. There are no explicit mentions of contributors, community channels, or a roadmap for future development.

Licensing & Compatibility

The repository does not specify a license. It is a collection of links to external resources, and users should adhere to the licenses of those individual resources.

Limitations & Caveats

The roadmap is a passive compilation of links and does not offer interactive tools or direct learning content. The quality and availability of external resources are not guaranteed by this repository.