Mathematics for Machine Learning, Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, 2020 (Cambridge University Press)DOI: 10.1017/9781108679901 - This book bridges the gap between foundational mathematics and machine learning, offering dedicated chapters on vector calculus, linear algebra, and optimization, directly relevant to understanding multivariable optimization in ML contexts. Available freely online.
Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press)DOI: 10.7551/mitpress/9780262035613.001.0001 - A comprehensive textbook on deep learning that includes detailed discussions on optimization algorithms, the challenges posed by saddle points, and the computational implications of the Hessian matrix in high-dimensional parameter spaces. Available freely online.