I don't see what we gain for the mention of category theory here, and I find the categorical content in the book to be pretty buried.

If we're talking about categories, then we should be able to provide definitions of objects and morphisms clearly and independently. I guess I expected this to give denotational semantics of machine learning in an appropriately structured category, and then afterwards we can provide an implementation of these abstractions in Rust; however, this doesn't seem to be the case in this book. Rather, the category theory does seem somewhat stapled on. I wished that this would talk about things like Markov categories (or some other appropriate appropriate semantic domain) and then characterized machine learning algorithms via adjunctions between certain categories, such as in https://link.springer.com/article/10.1007/s44163-025-00707-w

As it's written, I don't see much of an opportunity for deriving theorems about the implementation from abstract nonsense, which, to me, would be the biggest strength of such a categorical description. This seems to be a simultaneous high-level introduction to Rust, machine learning, and category theory. The writing suffers from this, as the reader doesn't have much of an opportunity here to detangle these ideas from each other or see how one aids in understanding the others. Instead, they are all provide at once, and to an insufficient level of detail (for the amount of skimming that I did).