His geometry-centric approach to linear algebra was exactly what I needed to finally grok the subject. Topics like matrix multiplication and discriminants went from "why are they defined like this? it makes no sense?" to "of course that's how you multiply matrices because it's the only logical answer".
It's only later that I discovered Wildberger has some ~strange~ very interesting ideas regarding imaginary numbers, but these ideas don't detract one bit from his presentation of linear algebra. Highly recommended viewing for anyone who is keen on neural networks and machine learning but struggles with understanding the underlying mathematics.
It covers many different fields in a shallow, introductory way (which keeps it quite approachable to non-experts). It's following a textbook, but he of course gives his own idiosyncratic takes on things.
It's not just imaginary numbers, the ultrafinitist rabbit hole runs deep. But there's nothing inherently wrong with it, it's just a different set of axioms than everyone else uses. With those constraints you can still do meaningful things by other means (checkout his rational trigonometry stuff), at the expense of more cumbersomeness.
https://www.youtube.com/playlist?list=PLIljB45xT85BhzJ-oWNug...
His geometry-centric approach to linear algebra was exactly what I needed to finally grok the subject. Topics like matrix multiplication and discriminants went from "why are they defined like this? it makes no sense?" to "of course that's how you multiply matrices because it's the only logical answer".
It's only later that I discovered Wildberger has some ~strange~ very interesting ideas regarding imaginary numbers, but these ideas don't detract one bit from his presentation of linear algebra. Highly recommended viewing for anyone who is keen on neural networks and machine learning but struggles with understanding the underlying mathematics.