This repository serves as a curated collection of deep learning projects, resources, and tutorials, primarily targeting data scientists, machine learning engineers, and AI enthusiasts. It aims to provide practical examples and guidance for various cutting-edge AI domains, including LLMs, NeRFs, and generative AI, with accompanying video content.

How It Works

The project showcases a diverse range of deep learning applications, from satellite imagery analysis and face stylization to text-to-image generation and audio transcription. It leverages popular frameworks like TensorFlow, PyTorch, and Hugging Face, demonstrating model training, inference, and deployment. The content is structured around specific AI topics, often with multi-part video series linked to the code.

Quick Start & Requirements

• Installation and execution vary by project; specific instructions are typically found within individual project subdirectories.
• Prerequisites include Python, deep learning frameworks (TensorFlow, PyTorch), and potentially CUDA for GPU acceleration.
• Some projects may require specific datasets or API keys.

Highlighted Details

• Comprehensive coverage of LLMs, including research, open-source models (Bloom 176B), and building ChatGPT-style applications.
• In-depth exploration of NeRFs (Neural Radiance Fields) with projects and open-source models.
• Practical examples of generative AI, including Stable Diffusion (text-to-image, video) and DALL-E Mini.
• Tutorials on essential deep learning infrastructure, such as CUDA installation and environment setup for TensorFlow and PyTorch.

Maintenance & Community

The repository is maintained by Avkash Chauhan (@avkashchauhan) and promoted via the Prodramp YouTube channel and Twitter. Links to LinkedIn and personal blogs are also provided for direct contact and further resources.

Licensing & Compatibility

The repository's licensing is not explicitly stated in the provided README. Users should verify compatibility for commercial or closed-source use.

Limitations & Caveats

The repository is a collection of diverse projects, and setup complexity, dependencies, and code quality may vary significantly between individual components. Some projects might be experimental or research-oriented, potentially lacking robust documentation or production-readiness.