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Note that this approximated "derivative" has size n-1 where n is your array/list size. Don't know what you are trying to achieve but here are some ideas Please be aware that there are more advanced way to calculate the numerical derivative than simply using diff.
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In order to compute derivatives in our neural network, we generally call backward on the Tensor representing our loss. Then, we backtrack through the graph starting from node representing the grad_fn of our loss. As described above, the backward function is recursively called through out the.
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Answer (1 of 7): I wouldn’t. This is the kind of problem that lends itself so easily and naturally to generalization that I would view it as a waste to solve it for n=4. More than a waste, the details of the computation obscure the essence of it; it’s clearer to see the essence in the general cas.