Summary

This repository offers a sequential series of Jupyter notebooks guiding users from foundational deep learning concepts to building and understanding advanced models like GPT. It targets engineers and researchers seeking a practical, hands-on approach to mastering modern AI architectures.

How It Works

The project employs a structured, notebook-driven curriculum progressing logically through core deep learning principles. It starts with fundamental building blocks, moves to neural networks, explores specialized architectures (CNNs, ResNets), and culminates in advanced topics: image-to-text generation, GPT implementation, inference, and Hugging Face fine-tuning. This pedagogical approach ensures a deep, practical grasp of model development.

Quick Start & Requirements

Setup: Clone repo, create/activate uv virtual environment, uv pip install -r requirements.txt, launch with uvx jupyter lab. Prerequisites: Python 3.8+, uv. macOS (Apple Silicon) uses PyTorch MPS. Linux with NVIDIA GPUs requires NVIDIA drivers/CUDA Toolkit; dependencies are CUDA-configured. unsloth library integrated for speedups. Specific unsloth versions (e.g., unsloth[cu121-py310]) may need manual installation per CUDA version. Consult official uv and unsloth documentation.

Highlighted Details

• Step-by-step guidance for building and understanding the GPT architecture.
• Practical modules on fine-tuning models using the Hugging Face ecosystem.
• Integrates unsloth for substantial performance enhancements on NVIDIA GPUs.
• Curated learning path from fundamentals to state-of-the-art generative models.

Maintenance & Community

No specific details regarding maintainers, community channels, or project roadmap were present in the provided README snippet.

Licensing & Compatibility

License type and compatibility notes for commercial use or integration with closed-source projects were not specified in the provided README snippet.

Limitations & Caveats

Linux setup on NVIDIA GPUs requires careful configuration of host drivers and CUDA Toolkit for optimal unsloth integration. Users must consult unsloth documentation for correct installation commands matching their CUDA version, potentially causing setup friction. Repository scope is limited to notebooks; external integrations or production deployment are not detailed.