Explores data augmentation as a key regularization method in deep learning, covering techniques like translations, rotations, and artistic style transfer.
Covers sampling, transformations, acquisition models, aliasing problems, and examples in image processing.
Explores the similarity of convex bodies, affine transformations, the Johen's Theover theorem, and KKT conditions.
Explores convex functions, including checking convexity, transformations, examples, minimization, geometric intuition, Schur's Lemma, distance function, perspective function, and relative entropy.
