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The same is true of backprop in deep learning -- knowing how to code up backprop by hand gives you no useful knowledge wrt deep learning, and inversely, developing powerful mental models for deep learning does not in any way require knowing the algorithmic details of backprop
(coming from someone who had to implement backprop a lot in the past, first in C, then in Matlab, then in Numpy)
In addition, if you have the right mental model for something, it is generally easy to work out the algorithmic details on your own when you need them, at least down to a level where you can roll out a working implementation (& it becomes trivial if you can just look up details)
Similar to how, say, you can always reinvent the Pythagorean theorem on the fly if you think about geometry through the lens of vector products, or how you don't need to memorize the quadratic formula if you understand what an equation is and the general process for solving them
Its easy to say once you have the knowledge of it to easily dismiss it. Perhaps pioners of fields must also mention, if one wants to use some tools without knowing its ramifications or not. Mathemarics is required, so is statistics else its monkey see monkey do
I agree once an algorithm is settled science the exercise is academic, never-the-less those who thirst for knowledge enjoy the added understanding; having said that your point about mental models is undeniable
And I think this is the point. If I don't know the ins and outs from a mathematical viewpoint I can't know how much of it I need to know. The irony is that your knowledge gives you the privilege of disregarding those same details.
Is the c code open
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