Exercise book to complement Mathematics for Machine Learning I was learning the book Mathematics for Machine Learning by Marc Peter Deisenroth. While the book is rigorous and well-structured, it lacks a great amount of exercise. Are there any similar resources that I can get my hands dirty? (In particular, the chapter for linear algebra and vector calculus will be great.
 A: The companion webpage to the MfML book contains this link to additional exercises. Though there are only three questions on linear algebra (Ch. 2) and one question on vector calculus (Ch. 5). The webpage also contain links to third-party Jupyter notebooks that implements and visualises the examples used in the book if that is your thing.
The main text of the MfML book (link to PDF made available by the authors) also provided some textbook references, e.g. in Section 2.9:

There are many resources for learning linear algebra, including the textbooks by Strang (2003), Golan (2007), Axler (2015), and Liesen and
Mehrmann (2015).

I haven't check the referenced textbooks, but surely at least one of them would have contained more exercises.
Prior to this book being published, the recommended mathematical methods textbook for Computing students at Imperial College London (where Marc Deisenroth taught) is G. Stephenson (1973) Mathematical Methods for Science Students. Chapters 16, 17, and 19 (and their exercises) should be relevant for linear algebra and vector calculus.
