So I skimmed a lot of that book (by Matthew Rosch). Actually I’ve worked through it before. It has quite a nice complete worked example at the end of a Linear Regression problem. Also, after describing the basic steps in setting u a feed forward network (multi layer perceptron) he also describes RNN, CNN and a couple of variants, which I’ll consult if I’m ever working with one of those.
Somewhere he writes that to solve a problem with a neural network requires intuition, domain knowledge, and experience. I guess that cuts me out of most use cases. Oh, and lots of trial and error of course. As with all things ML. Still, I’ll explore a little further. I haven’t seen any guidelines on the architecture of a neural network, how many nodes, when to use what activation function. All just trial and error I guess. No hints? Oh well…