As a professional, does it worth it to learn the math behind NN?

My personal feeling is that it's always "worth it" to learn more maths. Not saying it's absolutely required, and I'm a big fan of (generally speaking) prioritizing the pragmatic / practical / hands-on over the purely theoretical. But, even within that belief system, I would say that - all things being equal - any opportunity to learn more maths is worthwhile, at least up to a certain point. Figuring out the details of the "certain point" is left as an exercise for the reader though.

I recommend learning multi variable calculus and linear algebra. You can take courses online for free. Personally, I didn’t really understand what was going on until I refreshed my math.

It won’t make you an ML expert, but it’s like the difference between knowing how to write a sort function vs. only knowing how to call a sort function. Ideally, you understand both. Not saying you need to understand how every sort function works internally and write a proof for it… but a basic understanding is helpful.

Since you're working in the field - yes, obviously. It's not even that much math - the building blocks are fairly straightforward linear algebra and calculus.

Here's a nice, fairly detailed, summary - https://explained.ai/matrix-calculus/. But if you haven't taken undergraduate level single/multivariable calculus and linear algebra, I would take them from MIT Opencourseware even if it's just to fight off the impostor syndrome a little bit.

Frankly, I rarely see this complicated math in academic papers to be useful in practice.

Most of the stuff that is valuable in practice does not require much math.

Many people believe math actually hindered the development of AI.

How much do you know already?

there is not much math, don't let the academic papers razzle dazzle you, they make things look complicated to get their grants

in practice, as long as you've studied basic calculus and understand how to find a minimum of a function via derivative you're good, there is your "gradient descent" in a nutshell: https://www.mathsisfun.com/calculus/maxima-minima.html

everything else is plug-and-play from existing libraries

you can ask any "data scientist" or "ML engineer" what they do all day, it's a whole lot of copy paste, and tweaking the data and parameters through trial and error until it fits

Edit: Ok , it would also help to understand dimensionality reduction via PCA/SVD at least once, it's available in any linear algebra book: https://en.wikipedia.org/wiki/Singular_value_decomposition , https://en.wikipedia.org/wiki/Principal_component_analysis that's probably the best and most "scientific" part of ML