Author of Learning Deep Learning here.
How to solve a practical problems requires much more well-rounded skills that mastering one machine learning algorithm or another (in fact, some problems don't require ML at all).
For a more general introduction to data science, see http://p.migdal.pl/2016/03/15/data-science-intro-for-math-phys-background.html. So yes: discussing things with clients, getting data, cleaning data, realising it is not enough, so asking client if they have more/different data, exploring it, seeing that some of it is rubbish, semi-manually cleaning it, creating a model, seeing it's ok, discovering that it fitted to some artefact, ... (and dozens, dozens of steps).
"In fact, my biggest regret is delaying learning it, because of the perceived difficulty. To start, all you need is really basic programming, very simple mathematics and knowledge of a few machine learning concepts. I will explain where to start with these requirements.
In my opinion, the best way to start is from a high-level interactive approach. For that reason, I suggest starting with image recognition tasks in Keras, a popular neural network library in Python."
Sometimes I feel that these deep-learning tutorials I encounter on the web (of which I've encountered a great many) usually don't mention how little time will be spent actually designing and running a deep-learning model.
The problem is that, in "the wild", you almost never encounter any situations that resemble the scenarios presented in these tutorials. For example, for our company, a typical project encountered might look like this:
I will add a caveat that:
I sometimes wonder if we're getting an unusual subset of major corporations with these characteristics, but these are pretty major, large firms that seem to share many of the same business practices with each other, so I would somewhat doubt that.
But in general, I think that there seems to be a far larger share of articles covering how to do basic things with Keras and Tensorflow, and too few on the "hard problems" of data science work.