Mini-batch gradient descent (GD) is a fundamental optimization technique in machine learning, where the i.i.d. (independent and identically distributed) assumption for data plays a critical role. When this assumption is violated, several challenges arise that can affect the model’s performance and training efficiency. This article explores the implications of non-i.i.d. data in mini-batch…
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