I don't think it is, as somebody who's spent maybe 100 combined hours reading AI papers mostly focused around NLP and image classification.
You have a dataset, symbolically represented in 1s and 0s. You have an objective function (e.g. classify the object as belonging to one of N categories).
The purpose of the collective neurons in the network is to "encode" the input space in a way that satisfies the objective function. In the same way that we "encode" higher-level concepts into shorthand representations.
Gradient descent is the optimization function we use to develop this encoding.
Beyond this, there are all kinds of tricks people have developed (interesting activation functions for neurons, grouping + segregating neurons, introducing a dimension of recurrence/time, dataset pre-processing, using bigger datasets, having another model generate data that's deliberately challenging for the first model) to try to converge to a more robust/accurate encoding, or to try to converge to a decent encoding at a faster rate.
There is no magic here at the lowest level – you can interrogate the math at each step and it'll make sense.
The "magic" is that we have zero epistemology to explain why tricks work, other than "look, ma test results". We know certain techniques work, and we have post-hoc intuitive explanations, but we're mostly fumbling our way "forwards" via trial and error.
This is "science" in the 17th century definition of the term, where we're mixing chemicals together and seeing what happens. Maybe we'll have a good theoretical explanation for our experimental results 100 years from now, if we're still around.