This repository serves as a comprehensive, curated hub for Generative AI research, learning, and application development. It targets AI researchers, engineers, and students seeking to stay updated on the latest advancements, prepare for interviews, or find resources for building GenAI applications. The primary benefit is a centralized, regularly updated collection of high-quality, diverse resources, saving users significant time in navigating the rapidly evolving GenAI landscape.

How It Works

The repository is structured into thematic sections, including monthly paper summaries, interview preparation materials, curated lists of free courses, and code repositories/notebooks. It emphasizes practical application and learning, featuring roadmaps for specific topics like RAG and LLM foundations, alongside detailed course materials and a broad collection of academic papers with summaries.

Quick Start & Requirements

• Installation: No direct installation required; it's a curated list of external resources.
• Prerequisites: Access to the internet to view linked resources. Some linked courses or code repositories may have specific software or hardware requirements (e.g., Python, specific libraries, GPUs).
• Setup Time: Immediate access to information.

Highlighted Details

• Monthly curated lists of top GenAI papers with abstracts.
• Extensive lists of free GenAI courses and roadmaps for learning specific topics.
• Interview preparation resources, including common questions and topic-wise breakdowns.
• Links to code repositories and tutorials for building GenAI applications.
• Includes materials from "Applied LLMs Mastery 2024" and "Generative AI Genius 2024" courses.

Maintenance & Community

• The repository is actively updated, with a focus on monthly paper summaries.
• Contributions are encouraged via Pull Requests.
• A citation format is provided for academic referencing.

Licensing & Compatibility

• The repository itself is licensed under the MIT License.
• Linked resources will have their own respective licenses, which users must adhere to.

Limitations & Caveats

The quality and availability of linked external resources are dependent on their original sources. While curated, the sheer volume of information means users must still exercise judgment in selecting and utilizing the provided materials.