Comment by miki123211
12 hours ago
Machine learning is more like biology, physics or law than like CS or math.
In the software engineering world, we're accustomed to the reality that to do great work, all you need is a sharp mind and a cheap laptop. Better tools help, but aren't really a necessity. You could probably become a top-notch Linux kernel engineer with nothing but dedication and self-study, a $300 laptop and a shabby apartment with internet access.
You just can't really learn ML that way, just like you can't do it with banking, professional trading or medicine.
Kind of sounds like a truism. You make the claim, but you don't explain why. Also, the analogies at the end are... odd.
Maybe you are right! I'd like to hear the justifications more though.
Simply put, I could compile and build from the source of most of the exciting cutting-edge software for free as a hobbyist once I own the computer.
But I can't do the same for things that excite me in this DL domain, even running pretrained models requires hardware that most hobbyists can't afford.
Cloud solutions exist but unlike traditional dev where we could test almost everything about the application locally, only thing unsolved was deployment and distribution in some cases, which is a sensible spend on infrastructure.
Weird to put banking and trading on the same complexity level as medicine or ML.
Agreed trading and banking are social systems, more complex that biological systems