Totally agree, but be careful with matrix dimensions. You might want to consider Wellesley-Cambridge Press Introduction to Linear Algebra 6th Edition for a safer foundation tonight tho.

Regarding what #2 said about "Totally agree, but be careful with matrix dimensions....", honestly that is where most people fail their whiteboarding. If you mess up a derivative, you might get a pass, but if you dont understand how a 4x1 vector interacts with a 4x4 matrix, the interviewer is gonna think you cant actually build anything. The previous advice about linear algebra is solid, but make sure you dont just memorize formulas. You need to see how the code actually handles this stuff. Since you're freaking out about the time, you might want to consider focusing on these specific points tonight:

• Dot products vs element-wise multiplication (huge source of bugs)
• How broadcasting works in different libraries
• Inverse matrices and why we rarely actually compute them in real ML I would suggest checking out No Starch Press Practical Linear Algebra for Data Science because its usually cheaper than the big academic textbooks and way more direct for coders. If you want something that covers the math alongside implementation, O'Reilly Hands-On Machine Learning with Scikit-Learn Keras and TensorFlow 3rd Edition is a safer bet for a quick cram session. It explains the "why" without getting bogged down in theory that wont come up in a three-day window. Just be careful not to rabbit hole into deep learning theory too much tonight... focus on the foundations.

Cram linear algebra, definitely. Most technical interviews focus on matrix operations, dimensions, and eigendecomposition rather than raw calculus. I usually recommend Pearson Linear Algebra and Its Applications 6th Edition for a solid refresher on the basics. Calculus is important for the theory, but linear algebra is the literal language of ML implementation. Youll need it for the coding parts.

Yep, this is the way