3,492 karma · joined January 2, 2019
This was a question [0] I asked on stack overflow more than 15 years ago, and is to this day the most up votes I've gotten on SO. I still get notifications from it occasionally.
[0] https://tex.stackexchange.com/questions/11123/prevent-pdflat...
[0] https://www.seashepherdglobal.org/latest-news/slaughter-dolp...
You're changing the definition of "better" so you can have your cake (admit X is better than Y) and eat it too (prefer Y over X). It's ok to like fun things! It's ok to like Imperial and weird grammar, but then you can't also claim that you agree it's worse.
When I made a meme about C++ [1] I was purposeful in choosing the iceberg format. To me it's not quite satisfying to say that C++ is merely complex or vast. A more fitting word would be "arcane", "monumental" or "titanic" (get it?). There's a specific feeling you get when you're trying to understand what the hell is an xvalue, why std::move doesn't move or why std::remove doesn't remove.
The Forest Gump C++ is another meme that captures this feeling very well (not by me) [2].
What it comes down to is developer experience (DX), and C++ has a terrible one. Down to syntax and all the way up to package management a C++ developper feels stuck to a time before they were born. At least we have a lot of time to think about all that while our code compiles. But that might just be the price for all the power it gives you.
The Hessian shouldn't have been called a matrix.
The Jacobian describes all the first order derivatives of a vector valued function (of multiple inputs), while the Hessian is all the second order derivatives of a scalar valued output function (of multiple inputs). Why doesn't the number of dimensions of the array increase by one as the derivation order increases? It does! The object that fully describes second order derivation of a vector valued function of multiple inputs is actually a 3 dimensionnal tensor. One dimension for the original vector valued output, and one for each derivation order. Mathematicians are afraid of tensors of more than 2 dimensions for some reason and want everything to be a matrix.
In other words, given a function R^n -> R^m:
Order 0: Output value: 1d array of shape (m) (a vector)
Order 1: First order derivative: 2d array of shape (m, n) (Jacobian matrix)
Order 2: Second order derivative: 3d array of shape (m, n, n) (array of Hessian matrices)
It all makes sense!
Talking about "Jacobian and Hessian" matrices as if they are both naturally matrices is highly misleading.
* Absolutely never any beep or sound
* Direct controls, no "programs" (i.e. microwave has two knobs: power and time, etc.)
* No network connectivity of any kind (obviously)
With a strong brand identity and good marketing these would sell like sliced bread.
I'm a hard group 1, and I don't really mind if LLMs take my job, just please don't take my passion.
is now my favourite xkcd 2326 example.