> I know, DL works only if you have massive datasets, DRL is even worse for the number of training episodes. Maybe you've heard about recent craziness of using GANs to generate training set fillers when your training sets are small, i.e. you have only 1,000 examples but need 10,000 for reasonable performance. Instead of gathering more examples, you use GAN to create believable training data, and it seems to be working quite well (i.e. a bump from 60% accuracy to 80% while bigger training dataset with real examples would bump you to 90%).

Sounds a bit like Baron Münchhausen pulling himself and the horse on which he was sitting out of a mire by his own hair.

I'd assume that instead of pulling such stunts, a reasonable generative model might have done the trick.

> What I observed is that many ML companies now run two pipelines in parallel, one based on Deep Learning and the other on classical ML, then cherry pick solutions that work best for the problem/scale they have.

Putting it this way, I agree. And my personal addendum here is: classical ML outperforms DL more often than the hype might make people think.