This is widely considered the gold standard. It dedicates an entire pillar to , covering exactly what you need for ML—gradients, partial derivatives, and the Chain Rule—without the fluff of a traditional 3-semester college sequence.
: A concise "refresher" document from designed for computer science students to quickly catch up on continuous math from an ML perspective [4]. Why Calculus Matters in ML calculus for machine learning pdf link
Another excellent free resource: by Terence Parr and Jeremy Howard – 👉 https://explained.ai/matrix-calculus/ (HTML + free PDF download from the page). This is widely considered the gold standard