network so again just reiterating what I just said if you think about a set of parameters of a deep network layer they're gonna automatically induce a partition of the input space of that particular layer into convex regions and then if you compose several of these layers we're going to form a non convex partition of of the input space and and this provides really interesting non-trivial links to classical ideas out of signal processing information theory computational geometry namely ideas like vector quantization k-means and and