https://marovi.ai/index.php?action=history&feed=atom&title=Translations%3AStochastic_Gradient_Descent%2F3%2FenTranslations:Stochastic Gradient Descent/3/en - Revision history2026-04-27T22:02:15ZRevision history for this page on the wikiMediaWiki 1.39.1https://marovi.ai/index.php?title=Translations:Stochastic_Gradient_Descent/3/en&diff=3307&oldid=prevFuzzyBot: Importing a new version from external source2026-04-27T00:35:51Z<p>Importing a new version from external source</p>
<p><b>New page</b></p><div>In classical {{Term|gradient descent}}, the full gradient of the {{Term|loss function}} is computed over the entire training set before each parameter update. When the dataset is large this becomes prohibitively expensive. SGD addresses the problem by estimating the gradient from a single randomly chosen sample (or a small '''{{Term|mini-batch}}''') at each step, trading a noisier estimate for dramatically lower per-iteration cost.</div>FuzzyBot