This is the second lecture on Autoencoders in Deep Learning. It begins with the manifold hypothesis, which says that natural laws constrain data to lie on low-dimensional manifolds. A manifold is mathematically defined in terms of topology and neighborhoods. Although manifolds can be specified by tiling of tangent planes, contractive autoencoders which use regularization specified by a Jacobian term perform better.
