Here’s every little thing it’s good to know (beyond the usual definition) to master the numerical derivative world
There may be a legendary statement that you could find in at the least one lab at every university and it goes like this:
Theory is when you already know every little thing but nothing works.
Practice is when every little thing works but nobody knows why.
On this lab, we mix theory and practice: nothing works and no person knows why
I find this sentence so relatable in the info science world. I say this because data science starts as a mathematical problem (theory): it’s good to minimize a loss function. Nonetheless, once you get to real life (experiment/lab) things begin to get very messy and your perfect theoretical world assumptions may not work anymore (they never do), and also you don’t know why.
For instance, take the concept of derivative. Everybody who deals with complex concepts of knowledge science knows (or, even higher, MUST know) what a derivative is. But then how do you apply the elegant and theoretical concept of derivative in real life, on a loud signal, where you don’t have the analytic…
