Batch size refers to the number of training examples utilized in one iteration of model training. This concept is crucial as it directly impacts how models learn from data and influences the overall efficiency of the training process. The choice of batch size affects memory usage, the stability of gradient updates, and ultimately, the performance of the model during and after training.
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