Abstract
Abstract In this article, we introduce gradient descent and stochastic gradient descent for solving large‐scale statistical and machine learning problems. We introduce in detail how to derive gradient descent and stochastic gradient descent algorithms, briefly highlight their convergence properties, and show how to apply them to solve statistical and machine learning problems. In the end, we also discuss some open problems when applying stochastic gradient descent to train large‐scale machine learning models.
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