Explore deep neural network understanding in this CVPR'20 iMLCV tutorial video. Delve into applications of deep learning, research themes, and perturbation approaches. Learn about meaningful and extremal perturbations for interpretability, examining foreground evidence and background suppression effects. Discover techniques for adversarial defense, including regularization and smooth masks. Compare with prior work, focusing on weak localization performance and output class selectivity. Investigate intermediate activations, spatial and channel attribution, and activation "diffing". Analyze concepts per filter and filters per concept in self-supervised learning. Examine concept embedding spaces for segmentation and classification tasks. Gain insights into human-guided machine learning and future directions for model debugging.