[throwawaybbq1]

No .. when you do MNIST in a DL tutorial it all looks easy. The problem is when things don't work as expected (real-world TM). I have a freakin PhD in CS (top school where a bunch of this stuff was invented .. some of my friends are "Gods" now) and did a bunch of basic ML in my thesis but ... I never got deep into the math (my PhD was in something else .. a math-lite field). I know a metric space but do understand it well enough? What the hell are kernel smoothing methods? Laplacians, Jacobians, what, what, what??? (Fine .. I was joking with the last ones .. I remember vector calculus) I work in AI and feel I study ALL-THE-TIME. Please don't torture yourself like I did. Get a proper Masters (but realize you need a PhD in the specific sub-field and strong math chops).

Rant Over .. I also had a thought for the OP. In Web dev (and other kinds of SW dev), you kick it hard enough and it works. I had a job offer at a famous company where they wanted me to do a vision model to detect theft. I got the offer but didn't take the job .. one factor was if it didn't work with my existing toolbox, what does that mean for me? Do I get fired or I quit myself? This was a serious question I posed to myself.

In research gigs, you take on hard problems and try your best. In an industrial/startup setting, AI of hard problems requires incredible training and self-confidence at the leadership level. A data engineer does not require this hard stuff and you just work under some scientist. But be aware the turn around time for experiments is very fast (like the scientists had 5 new ideas on the whiteboard by the time the dev team walked from the conf room back to their desks). I don't even know how my engineers keep up with it.

I think AI jobs where you do actual innovation/exploration are incredibly hard and require a ton of investment (personally in terms of a near 24/7 job and your employer). Even then success isn't guaranteed. I think there are much easier jobs out there (like Cloud, Security) that have an equally bright career outlook .

[mbeex]

> Laplacians, Jacobians, what, what, what???

Can you really get a PhD in anything even remotely STEM-related these days without at least knowing what those things are? I'm growing old...

[brutus1213]

Parent said they were joking about that example. Not every STEM PhD is math heavy. CS itself is big field with subareas that may or may not require fluency in topics such as optimization (e.g. HCI, Databases). ML/DL is optimization heavy, no? So the point is valid I think.