There's a few books around for this, first is Jeremy Kun's book https://pimbook.org/ and is probably exactly what you're looking for: math from a programmer's perspective. There's also Discrete Math w/Functional programming https://cs.wheaton.edu/~tvandrun/dmfp/ in which most chapters translate theorems into algorithms or turn sets into types. Sussman also has two books translating Lagrange equations and differential eq into Scheme but both assume an undergrad physics background. Brown has a course in linear algebra using Python http://cs.brown.edu/courses/cs053/current/lectures.htm but you can do it with any math language library that builds matrices or write your own naive implementation as you go.
Of course the best way to do this would be to try and model the notation yourself (if possible) into function specs or algorithms, as you go through various math books. Then you'd really understand the notation even if you failed to implement it. You could try Pyret to do this https://papl.cs.brown.edu/2018/func-as-data.html#%28part._.A...
Of course the best way to do this would be to try and model the notation yourself (if possible) into function specs or algorithms, as you go through various math books. Then you'd really understand the notation even if you failed to implement it. You could try Pyret to do this https://papl.cs.brown.edu/2018/func-as-data.html#%28part._.A...