Mini-batch stochastic gradient descent: Revision history

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20 March 2023

  • curprev 11:4311:43, 20 March 2023‎ Walle talk contribs‎ 3,804 bytes +3,804‎ Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, '''mini-batch stochastic gradient descent''' ('''MB-SGD''') is an optimization algorithm commonly used for training neural networks and other models. The algorithm operates by iteratively updating model parameters to minimize a loss function, which measures the discrepancy between the model's predictions and actual target values. Mini-batch stochastic gradient descent is a variant of stochastic g..."
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