[bigred100]

I don’t know much about industry but it seems to me there are two ways to look at ML. First is you learn some of optimization, stats, math, parallel computing, numerical methods. The second is that you learn a hell of a lot about fiddling with different network architectures and applying things to specific problems. I wonder whether the first (more fundamental) approach doesn’t have different prospects. At least in this case it can lead to career paths like a national lab.

[Ragib_Zaman]

The first path lets you become a quantitative problem solver, which is widely employable. The second path leads to being very good at specific deep learning tasks that I don't think will be in large demand in 5 years IMO, at least outside the largest tech companies with all the data. Other companies will fulfill their business needs with fewer ML-specialists, AutoML and pretrained models.