Here you'll find everything you need to know (beyond the standard definition) to master the world of numerical derivatives.
There is a legendary A statement that can be found in at least one laboratory at every university and it goes like this:
Theory is when you know everything but nothing works.
Practice is when everything works but no one knows why.
In this laboratory we combine theory and practice: nothing works and nobody knows why.
I find this phrase So identifiable in the world of data science. I say this because data science starts as a mathematical problem (theory): It is necessary to minimize a loss function. However, when it comes to real life (experiment/laboratory) Things are starting to get really complicated and your assumptions about a perfect theoretical world might not work anymore (they never do) and you don't know why.
For example, let's take the concept of derivativeAnyone who works with complex data science concepts knows (or, better yet, HAS TO know) what a derivative is. But then as Do you apply the elegant and theoretical concept of derivative? In real lifein a noisy signal, where you don't have the analytics…
