"Ground up" might be the wrong term here. I don't have right words either, but I feel GP is talking about that level between full knowledge and the "I have no idea what I am doing" level of downloading models from Kaggle, stuffing them into TensorFlow and calling yourself a "Deep Learning expert".
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.
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.