This repository provides a comprehensive historical overview of deep learning, organized around key milestones from early neural networks to modern large language models. It includes critical paper references, PyTorch implementations of key concepts, and educational notes covering data constraints, parameters, optimization, regularization, architecture innovations, and compute scaling. The content is structured as an annotated learning resource inspired by the history of machine learning research.