[38kkdiu]

Looks very nice, although it always takes me a bit to figure out what they're talking about with these sorts of things because I have to remind myself that most ML/DL stuff is supervised. What I research is unsupervised.

They kind of have this weird dissing of unsupervised scenarios, though. It's not like supervised or unsupervised is better or worse, they're just surrounding different problems. They can talk up their product without needing to criticize a problem domain.

It's like if you were making motors for boats, and then started talking about "these crazy people who think it's better to fly." ???

[syllogism]

I see how that came across as obnoxious, so thanks for the perspective.

I do think there's a pretty common failure mode for teams who don't have much experience with ML, though. Teams who don't have much experience with ML often take "We don't have much data" as a parameter of their problem, and don't see that this is something they can decide to change. This can lead to a lot of time spent experimenting with different unsupervised approaches that are a poor fit for what they're trying to do.