This repository is a curated list of references and online resources for data science, machine learning, and deep learning. It serves as a comprehensive knowledge base for students, researchers, and practitioners looking to deepen their understanding and skills in these fields. The collection aims to provide easy access to high-quality courses, tutorials, cheat sheets, articles, papers, books, datasets, and tools.

How It Works

The repository is structured into numerous categories, each containing links to relevant external resources. These categories cover a wide spectrum of topics, from foundational machine learning concepts and specific deep learning architectures (like CNNs and Transformers) to practical applications in areas such as NLP, computer vision, recommender systems, and MLOps. The inclusion of links to official documentation, GitHub repositories, and academic papers allows users to delve into the technical details of various tools and methodologies.

Quick Start & Requirements

This is a static collection of links; no installation or execution is required. Users can simply browse the README file to access the resources.

Highlighted Details

• Extensive coverage of Transformer architectures, LLMs, and RAG techniques.
• Detailed sections on AWS SageMaker, NVIDIA technologies, and popular ML libraries (PyTorch, TensorFlow).
• Resources for system design, algorithms, interview preparation, and career development in AI/ML.
• A broad collection of datasets, both real-world and synthetic, for practical application.

Maintenance & Community

The repository is maintained by peggy1502. There is no explicit mention of community channels or active development beyond the initial curation.

Licensing & Compatibility

The repository itself does not contain code and is a collection of links. The licensing of the linked resources varies by their original source.

Limitations & Caveats

As a curated list, the quality and maintenance of external links are dependent on the original sources. The repository's content is static and may not reflect the very latest advancements without periodic updates.