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Stochastic gradient descent (often shortened to SGD), also known as incremental gradient descent, is an iterative method for optimizing a differentiable objective function, a stochastic approximation of gradient descent optimization. A 2018 article implicitly credits Herbert Robbins and Sutton Monro for developing SGD in their 1951 article titled "A Stochastic Approximation Method"; see Stochastic approximation for more information. It is called stochastic because samples are selected randomly (or shuffled) instead of as a single group (as in standard gradient descent) or in the order they appear in the training set.

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stob, stocah, stoccado, stoccata, stochastic, stochastical, stochastically, stock, stockade, stockannet

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"stochastic gradient descent." Anagrams.net. STANDS4 LLC, 2024. Web. 25 Apr. 2024. <https://www.anagrams.net/stochastic%20gradient%20descent>.

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