This repository is a comprehensive, community-driven collection of resources for machine learning and deep learning. It serves as a curated knowledge base for researchers, students, and practitioners looking to learn or deepen their understanding of ML/DL concepts, algorithms, tools, and applications. The primary benefit is providing a centralized, organized, and easily navigable hub of high-quality learning materials.

How It Works

The repository is structured into a detailed table of contents, categorizing resources by topic (e.g., Cheatsheets, specific frameworks like Keras/TensorFlow/PyTorch, GNNs, Transformers, DL Books, Courses, Papers, Tools, and applications in Genomics, Vision, etc.). It aggregates links to GitHub repositories, official documentation, video lectures, blog posts, and academic papers, offering a multi-modal learning experience. The organization aims to facilitate efficient discovery and access to relevant information.

Quick Start & Requirements

This is a curated list of links and resources, not a software package. No installation or execution is required. Access is via a web browser to view the GitHub repository.

Highlighted Details

• Extensive coverage of foundational ML concepts and advanced DL architectures like Transformers and GNNs.
• Dedicated sections for popular deep learning frameworks (TensorFlow, PyTorch, JAX) with numerous tutorials and examples.
• Strong emphasis on practical applications, including computer vision, natural language processing, and specialized domains like genomics.
• Links to influential papers, books, and courses from leading institutions and researchers.

Maintenance & Community

The repository is maintained by mdozmorov and encourages community contributions. It acts as a community hub, linking to various platforms for further engagement.

Licensing & Compatibility

The repository itself is licensed under the MIT License, allowing for broad use and distribution. Individual linked resources may have their own licenses.

Limitations & Caveats

As a curated list, the quality and maintenance of linked external resources can vary. Some links may become outdated or broken over time. The sheer volume of information may require significant time to navigate and digest fully.