I wonder why there's so much more emphasis on Linear Algebra over Calculus (I've seen a number of courses teaching LA to complement DL courses), given that without Calculus, it's hard to understand optimization and backpropagation. Rote memorization might help for copying the same network over and over but isn't enough when you have to customize it.
How do you know a stationary point is a local optimum? Eigenvalues of the Hessian matrix! Even though vector calculus is taught without assuming linear algebra, a lot of the material is coupled. Further, many of the "first steps" machine learning ideas are essentially linear algebra problems (e.g., linear regression, PCA, etc).
