Hey, I just started with the book and I have a question on formula 2.68. Here the constraint
is stated. But this would mean that D has to be a orthogonal matrix with n=l. And this would miss the whole point of the compression. At the beginning of section 2.12 it is explained that l<n, columns of D are orthogonal and
I've always wondered about deep learning's history, and had read in several articles that neural networks and back-propagation used to be popular in 1980s. Didn't know that there was another deep learning phase in the 1960s!