物理学与深度学习之间的联系

Max Tegmark's MIT CBMM talk on why deep learning works so well. He argues that the laws of physics and the symmetries in physical data make it compressible into small neural networks, tying physics to network structure.

Why HN likes it: HN recommends it as a clear argument for why deep and cheap learning succeeds, often alongside the associated paper.

“physical data tends to have symmetries, and these symmetries make descriptions of the data very compressible into relatively small neural circuits”