In the context of deep learning, a plateau refers to a period during training when the model's performance, often measured by the loss or accuracy, remains relatively constant over several iterations despite continued training. This stagnation can occur due to various factors, including an unsuitable learning rate or the model reaching a local minimum in its error landscape. Recognizing and addressing plateaus is essential for optimizing training and improving model performance.
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