It doesn't mean I have to read every single line of Tensorflow but being able to do that when it's needed. So that such tools won't be magical black box for me.

I would recommend reading "I, Pencil" http://www.econlib.org/library/Essays/rdPncl1.html to help put your mind at ease.

Not to overdo analogies but you dont need to rebuild your own internal combustion engine in a unique way to drive a car or to contribute improvements to a car. The more you understand how and why tensorflow works the more you can do with it. It depends whether you want to build on top of that platform and use it, or build on the concepts for something else.

Even though I lack the name for that level, here's how I would describe in qualitative terms some of its attributes:

- Knowing the basic lay of the land all the way down. That is, at least knowing most of the black boxes and what they do, even if you don't exactly know how they do it.

- Being able to solve your own problems, instead of running around like a headless chicken every time you hit a speed bump in your work.

- Being able to reason from that first-ish principles. You're able to sketch solutions within the scope of the extended domain, and as you begin implementing it and need to understand various blackboxes in more depth, the basic shape of your solution isn't usually invalidated by gained knowledge.

In every field the total knowledge set is always increasing, which is both empowering, because we stand on the shoulders of giants, and diminishing, because there is less low-hanging fruit. There is always more low-hanging fruit though, the trick is to see it hanging there. ML is a wonderful opportunity because the magical api’s can do far more than they’re currently used for.