Todays ML frameworks grapple with the problem of “jacobian-vector products” & “vector-jacobian product” as a consequence of understanding the interplay between gradients & derivative; and the application of the “chain rule”. All of those 3 concepts are fundamentally understood by being proficient in calculus.
While I’m being the hype-man for calculus I don’t mean to say proficiency in linear algebra or statistics is in any “less necessary” or “less useful” or “less challenging” or “less..” in any way.
I’m merely stating that, historically, calculus has been the unique branch of study for engineering. Statistics has always found value in many fields — business, finance, government policy etc.
Sure Linear algebra is one of those unique fields too — I kinda like to think of it as “algebra” in general and perhaps its utility has flowed in tandem with calculus. Idk. I haven’t thought super hard about it.
Todays ML frameworks grapple with the problem of “jacobian-vector products” & “vector-jacobian product” as a consequence of understanding the interplay between gradients & derivative; and the application of the “chain rule”. All of those 3 concepts are fundamentally understood by being proficient in calculus.
While I’m being the hype-man for calculus I don’t mean to say proficiency in linear algebra or statistics is in any “less necessary” or “less useful” or “less challenging” or “less..” in any way.
I’m merely stating that, historically, calculus has been the unique branch of study for engineering. Statistics has always found value in many fields — business, finance, government policy etc.
Sure Linear algebra is one of those unique fields too — I kinda like to think of it as “algebra” in general and perhaps its utility has flowed in tandem with calculus. Idk. I haven’t thought super hard about it.