Take Linear Algebra, for example. We’re taught to see matrices as "grids of numbers." But to a machine learning engineer, a matrix is often secretly a graph. When you see a matrix as an adjacency list: • Matrix multiplication becomes a way of counting paths between nodes. • Eigenvectors reveal the hidden clusters within a network. • Information flow in a Neural Network becomes a topological problem, not just an algebraic one. I’ve always found that once you see the connection between these two worlds, the "scary" math disappears and is replaced by intuition. In the most popular edition of The Palindrome, I break down this exact connection. Join 38k+ others building their intuition here: https://thepalindrome.org/p/matrices-and-graphs…