The reverse nature might be due to approaching the problem from practical application of machine learning technology rather than the higher level abstracts of than from mathematics/statistics.
Which is to say that the starting point is that of someone figuring out what is going on behind the scenes of a working software package rather than building up from first principles.
Curious if there is a link to a better explanation from a similar starting point.
My apologies for not being clear, I was wondering if there was something closer toward the Randall Monroe [edit] "thing explainer" end of the spectrum. The reason I was wondering is because machine learning is becoming something that people incorporate into a project as a library. The analogy I would draw is to something like TCP/IP where there are abstractions over routing and congestion control and where routing and congestion control are abstractions over the mathematics of scheduling and graph theory.
Maybe a better analogy might be cryptography where practical implications of entropy pool design are relatively esoteric despite the vastly less accessible mathematical nature of reliable one way encryption algorithms.
Which is to say that the starting point is that of someone figuring out what is going on behind the scenes of a working software package rather than building up from first principles.
Curious if there is a link to a better explanation from a similar starting point.