Lecture
This lecture covers the concept of data augmentation as a widely used regularization method in deep learning. It explains techniques such as translations, rotations, affine transformations, noise addition, elastic deformations, and artistic style transfer. The instructor emphasizes the importance of reflecting all known invariances of the task in the augmented data to improve model generalization.