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Boris Galerkin posted:I'm just not really sure how this is any different from newton's method or like basically any numerical method that minimizes an objective/loss function. Newton's method and other second-order techniques aren't really appropriate for NNs for a couple reasons, not least of which being that the Hessian is so enormous: even if you don't store it you still have to compute with it. Backpropagation is just a way of quickly computing the gradient of the objective function that works with the particular structure of NNs.
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Jun 15, 2022 19:51
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| # ¿ May 17, 2024 15:30 |
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