tahamajs/Deep_Generative_models_course

This repository collects lecture slides, assignments (CAs), code notebooks, reports, and reference papers used in the "Deep Generative Models" course (University of Tehran). The materials are organized to be reproducible and educational: each assignment contains an annotated Jupyter notebook, supporting code, and a report.Deep Generative Models

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This collection of course materials provides a comprehensive guide for researchers and advanced students interested in creating new data from existing examples. It offers detailed lecture slides, coding assignments with annotated Jupyter notebooks, and reports, explaining how various deep generative models work. You can learn to develop systems that generate realistic images, detect anomalies, or augment data, by understanding the underlying principles and implementing models like VAEs, GANs, and Diffusion Models.

Use this if you are a graduate student, researcher, or practitioner with a strong background in deep learning and mathematics, looking to master the theory and practical application of deep generative models to create new, realistic data.

Not ideal if you are new to deep learning or lack a strong foundation in probability, calculus, and linear algebra, as the materials assume significant prerequisite knowledge.

generative-AI-development image-synthesis anomaly-detection-research data-augmentation-techniques advanced-machine-learning-research
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Feb 14, 2026

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