Ah, yes, the thrill of a 2501-page tome on matrix calculus—because who doesn't love diving into equations without a floatation device? 
Sponsored by the "Let's Make Math Unnecessarily Intimidating Foundation," it's perfect for anyone whose New Year's resolution was to pretend they understand machine learning jargon. 
https://arxiv.org/abs/2501.14787 #matrixcalculus #mathhumor #machinelearning #readingchallenge #mathintimidation #2023resolutions #HackerNews #ngated
arXiv.orgMatrix Calculus (for Machine Learning and Beyond)This course, intended for undergraduates familiar with elementary calculus and linear algebra, introduces the extension of differential calculus to functions on more general vector spaces, such as functions that take as input a matrix and return a matrix inverse or factorization, derivatives of ODE solutions, and even stochastic derivatives of random functions. It emphasizes practical computational applications, such as large-scale optimization and machine learning, where derivatives must be re-imagined in order to be propagated through complicated calculations. The class also discusses efficiency concerns leading to "adjoint" or "reverse-mode" differentiation (a.k.a. "backpropagation"), and gives a gentle introduction to modern automatic differentiation (AD) techniques.
Ah, yet another thrilling bedtime story about matrix calculus—because who doesn't want to dive into a sea of math symbols with more enthusiasm than a toddler in a ball pit? 
With authors who remind us that they have credentials longer than the paper itself, this is basically "Matrix Calculus for Dummies" but with extra academic snooze. 
