[vivekn]

The problem with doing that is you keep reinventing the wheel. Use deep learning if that fits the problem, but keep learning about other things like how EM works, variational inference, graphical models etc on the side. One day you might find a problem where deep learning doesn't work as well as some of the other techniques. Sure there are data science jobs that can be done without much knowledge, but people tend to stop when they see math and are just happy to use some API. This IMO is a wrong approach.

[maciejgryka]

You're completely right. There's a cool quote from http://waitbutwhy.com/2015/06/how-tesla-will-change-your-lif...:

    "I’ve heard people compare knowledge of a topic to a tree. If you don’t fully get it, it’s like a tree in your head with no trunk—and without a trunk, when you learn something new about the topic—a new branch or leaf of the tree—there’s nothing for it to hang onto, so it just falls away. By clearing out fog all the way to the bottom, I build a tree trunk in my head, and from then on, all new information can hold on, which makes that topic forever more interesting and productive to learn about. And what I usually find is that so many of the topics I’ve pegged as “boring” in my head are actually just foggy to me—like watching episode 17 of a great show, which would be boring if you didn’t have the tree trunk of the back story and characters in place."

I think learning to use APIs is probably the way to start, but it definitely pays off to keep digging deeper as you go along.