Earlier quoted context omitted.
At the ODSC presentation I went to last year where the team presented they actually used a vision problem as their canonical example. It's hard to grasp without a concrete example, but it makes a lot of sense the way they explained it. For example, lets assume you want to identify something like a lung tumor. So you have many MRI images and they're all largely the same template of image. Using traditional image proce…
Yeah, but for some tasks, like recognizing a car, or something else you'd have to write pretty sophisticated code to even get to a reasonable result. It's far easier in those cases to use supervised learning to have a NN learn to do it for you.
That's btw why a lot of examples of ML today are ones where data is (i) simple for non-experts to label, (ii) non-private and therefore easy to outsource for labeling, and (iii) low rate of change (e.g. images for self-driving, basic NLP stuff for chat bots, etc)- this kind of data can be labeled cheaply and once, so hand-labeled training sets are (barely) economically feasible to build manually. However, most data is not that easy or cheap to label, needs to be relabeled constantly to adapt to change, and thus the investment in a programmatic approach is often far better even if certainly not push-button!