This repository contains Jupyter Notebook implementations for the book “Deep Generative Modeling.” It covers major deep generative model classes: mixture models, probabilistic circuits, autoregressive models, flow-based models, VAEs, GANs, energy-based models, score-based models, and large language models. The materials are designed for readers with a background in calculus, linear algebra, probability theory, and basics of machine learning and PyTorch, combining conceptual explanations with executable code examples.