i suggest that it's more important to collect results (theorems) than texts. course descriptions on university sites would be a good thing to scrape (and and N

i suggest that it's more important to collect results (theorems) than texts. course descriptions on university sites would be a good thing to scrape (and and NLP >>?) for both. WARN: the following is ranty. it turns out that, no matter where you find them, the results in math are always the same. Hahn-Banach is Hahn-Banach, whether you read Folland or Rudin. moreover, language and notation are very consistent across sources, and math tends to be very "optimized" for /human/ learning. i haven't read much ML theory (yet?), but my (uninformed) gripe about it so far is that the math feels "noisy" in much the same way that Java is often called a "noisy" language: things are often expressed using the minimal mathematical machinery (e.g. multivariate calculus rather than geometry for gradients). as a result, i find the ML stuff imposes a lot of cognitive overhead to decipher equations that could be written more simply. so while the ML community might challenge the programming community to learn more maths on a regular basis, as someone who studied math in school and has historically worked as a non-ML developer, i would challenge the ML community to develop the theory in such a way that it encourages a more intuitive understanding of mathematics rather than the bare minimum (multivar calc + linear algebra) machinery. ---- for all this i shall plug one (perhaps lesser-known?) multivar calc text that i found recently and i think looks pretty good: Casper Goffman's _Calculus of Several Variables_