This series of blog posts introduces multivariate calculus for machine learning. While the first few posts should be accessible to anyone with a high-school math background, the articles covering vector calculus require a basic understanding of linear algebra. If you need a refresher, I’d suggest you first check out my series on linear algebra. Most of my examples reference machine learning to understand how mathematical concepts relate to practical applications. However, the concepts are domain-agnostic.
I’ve used the following books and resources for writing these posts.
Mathematics for Machine Learning by Deisenroth, Faisal and Ong
Deep Learning by Ian Goodfellow , Yoshua Bengio , et al.
Calculus Made Easy by Silvanus THompson
The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, et al.
I want to thank the authors for creating these amazing resources and recommend picking them up if you want to explore these topics more in-depth.