Explore a comprehensive lecture on the geometric understanding of deep learning in biomedical image reconstruction. Delve into the theoretical framework that explains why deep learning architectures outperform classical algorithms in inverse problems. Discover the unified approach that optimizes CNN design for various applications. Learn about a generalized cycleGAN framework for unsupervised learning in inverse problems without matched training data. Examine experimental results from supervised and unsupervised neural networks in biomedical imaging reconstruction to verify the geometric understanding of CNNs. Cover topics including deep learning for image reconstruction, diagnosis, and analysis, classical methods for inverse problems, input space partitioning, Lipschitz continuity, ultrasound acquisition modes, CT reconstruction approaches, and unsupervised learning for accelerated MRI.