PBT is awesome but I would still like to have tests I can easily reason about when implementing a tricky algorithm. The way I see it, TDD is the specification, PBT is for finding gaps in my specification.
> I'd have to look up what the "/" in "M/L" means in the 5th reply (I didn't)
M/L means that M is a field-extension over L. A concrete example would be C/R (the complex numbers over the reals).
Algebraic number theory, in particular Galois theory, studies field extensions by looking at the group of symmetries: the field automorphisms of the larger field that fix the smaller field. For the concrete example above, the Galois group is a group with two elements: the identity function and the function that maps i to -i and keeps real numbers fixed. It's not a coincidence that the dimension of C as an R-vector space is the same as the size of this group (or that the degree of the polynomial that has i (and -i) as roots has degree 2).
To add to that my current struggle: Most stuff is geared towards conda and terrible engineering practices (requirements.txt and not even locked versions). If you want to actually build and deploy something on actual hardware, you are in a world of hurt. And even for training the dependency situation is so ridiculous that most repos come with their own Dockerfile.
I marvel at the cool machine learning demos but I'm kind of sick working on this stuff, tbh.