As an example, a bias design goes something like this: "Let's see, I'll pin the base at five volts with a resistor divider. The emitter will be 0.6V below that. Then the emitter current will be (5.0 - 0.6) divided by the emitter resistor. The collector current will be essentially the same, so I can pick the collector load resistor to give me an appropriate quiescent point and make sure the output impedance is less than a tenth of the input impedance of the following stage (so I can ignore the latter)."
Knowing a parameter in detail isn't nearly as important as knowing if that parameter matters in the scope of the final design.
The book is also pretty honest about that and goes to pretty good lengths to guide readers to deeper material if it's an area they want to understand in greater depth.
A true testament that even in there modern days the principals taught are still solid.
I have yet to get his classical mechanics book. Gonna have to pull the trigger on that soon.
Edit: Thanks for that - it's more detailed than the talks I attended (LibrePlanet).
It seems deep learning is in pretty much at that state now (with possible exception of quality). The problem is that this puts inherent limits on what can be done with it. The power of digital computing is the power of modular expansion of objects. Analog circuits and computers don't have that. And current trained deep learning models don't have combinability and modularity either.
A lot of specific engineering subfields involve this "intuition, mental models, and patterns learned over years of experience" but keeping that model of deep learning indefinitely would have to involve a vast proliferation of such subfields each with their limits as the differences in applying deep learning techniques to different subfields become evident.
And while we're on it, is there something similar to analog or digital designs?
A ton of high-quality engineering is done based on intuition, mental models, and patterns learned over years of experience. My hunch is that deep learning will be the same.
EDIT: Just reread, and I want to clarify. I'm not saying that analog design is at the same stage of development as deep learning, or that it is anywhere near as ad hoc. Deep learning probably has a long way to go, but it could potentially end up in a similar state where years of experience is critical and intuition rules.