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Getting Started
Environment Setup
Processing Images for Neural Networks
PyTorch, Automatic Differentiation, and Training Mechanics
Neural Networks for Imaging
From Machine Learning to Neural Networks
Convolutions, CNNs, and End-to-End Training
Residual Learning, Receptive Field, and UNet
Vision Transformers and Loss Design
Cross-Domain End-to-End Reconstruction
Generative Models
Deep Generative Models, VAEs, and GANs
Diffusion Models: DDPM and DDIM
Diffusion Models for Inverse Problems
Homeworks
Homework 1: End-to-End Reconstruction Before Generative Models
Homework 2: Generative Models for Inverse Problems
References
References
Repository
Open issue
Index
