fully agree. I remember at one point I studied the Perron-Frobenius theorem because I was analyzing a problem in a different domain but mathematically not compl

fully agree. I remember at one point I studied the Perron-Frobenius theorem because I was analyzing a problem in a different domain but mathematically not completely dissimilar to PageRank, and it was crazy to think that a trillion-dollar company had come out of that bit of math. > Many (if not most) ML practitioners would profit from thinking more about the information-theoretic links between lossy compression, regularization via norm, and channel capacity to this day. yes. don't forget compressed sensing there too