The answer
No. Basic gradient descent uses a direct scaled gradient, while methods such as Adam incorporate running statistics that change how parameter updates are calculated.
How do gradients help update a neural network’s parameters?
Do all optimizers use the same update formula?
More FAQs in How do gradients help update a neural network’s parameters?
A zero gradient means no first-order change is indicated for that parameter at the current point. It does not necessarily identify a useful global optimum.
Gradient clipping limits excessively large updates by constraining gradient magnitude. It can stabilize training, but it does not replace good data, architecture, or optimizer settings.
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