This repository provides a curated list of essential deep learning topics and resources, targeting individuals preparing for technical interviews in the field. It aims to consolidate learning materials from blogs, books, open courses, and research papers, offering a structured path to mastering core concepts and recent advancements.

How It Works

The repository organizes deep learning knowledge into logical categories, including foundational mathematics (linear algebra, probability), core neural network concepts (backpropagation, loss functions, optimizers), and specialized areas like computer vision and natural language processing. It links to high-quality external resources, enabling users to delve into specific topics with expert-level explanations and practical examples.

Quick Start & Requirements

This is a curated list of links and does not require installation. All resources are external and accessible via web browsers or video platforms.

Highlighted Details

• Comprehensive coverage from fundamental math to advanced architectures like Transformers and Diffusion Models.
• Links to seminal papers, insightful blog posts, and university-level courses.
• Includes resources for understanding model variants, training techniques, and emerging research.
• Provides links to popular generative models like Stable Diffusion and DALL-E.

Maintenance & Community

The project welcomes community contributions for adding new resources or notes via pull requests. Links to relevant blogs and research paper aggregators are provided for staying updated.

Licensing & Compatibility

The repository itself is not licensed. All linked resources retain their original licenses and terms of use. Compatibility depends on the individual linked resources.

Limitations & Caveats

This is a static list of resources and does not include code implementations or interactive tutorials. The quality and depth of understanding depend on the user's engagement with the linked external materials.