Covers normed spaces, dual spaces, Banach spaces, Hilbert spaces, weak and strong convergence, reflexive spaces, and the Hahn-Banach theorem.
Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.
Explores distribution and interpolation spaces, differential operators, Fourier transform, Schwartz space, fundamental solutions, Farrier transform, and uniform continuity.
Covers the concept of compression and constructing prefix-free codes based on given information.
Covers the concepts of compression and prediction using prefix-free codes and distributions.
Covers the basics of linear algebra, emphasizing the identification of subspaces through key properties.
