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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

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By Davide Evangelista

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