Covers the fundamentals of convex optimization, including mathematical problems, minimizers, and solution concepts, with an emphasis on efficient methods and practical applications.
Explores data augmentation as a key regularization method in deep learning, covering techniques like translations, rotations, and artistic style transfer.
