alt Hacker NewsMachine learning could need, and probably has needed, some unified math notation for the past 15 years IMO. With that said, it was worse back in the day - when ML papers were the products of researchers from all over, you'd see some wild notation.
Many will likely disagree with me, but inconsistent notation (across papers!) is to me friction. At least in this article the author explicitly explains the notation at the very start...that is not always the case. Rarely, even.
EDIT: Didn't even notice the notation switch, much appreciated.
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> At least in this article the author explicitly explains the notation at the very start
They explain one particular aspect of the notation but never define the variables used. What is k? q? S?
It's obvious if you've studied machine learning before, and for some of them you can make an educated guess, but it makes the article mostly opaque if you don't already have some domain-specific background knowledge.
I used to think this, then I realized that the amount of time you spend with equations is so much more than code, and the terseness makes them much easier to read once you know what the symbols are.
Also, letters avoid having to name them, naming being a hard problem and all.
I never understood people who preferred traditional math notation (e.g. single letter symbols, weird characters like ∣q⟩ instead of writing down an explicit type, etc.). I guess the main advantage is terseness? To me, the mathematical expressions would be so much easier to understand if they were just written in pseudo code or an actual programming language like Python.