Learning Rate
In machine learning, the learning rate is a hyperparameter that determines the step size at each iteration while moving toward a minimum of a loss function during training of a model. It controls how much to change the model in response to the estimated error each time the model weights are updated. A learning rate that is too small may result in a long training process that could get stuck, whereas a learning rate that is too large may result in learning a sub-optimal set of weights too fast or an unstable training process. Therefore, an appropriate learning rate is crucial for the successful training of a model.
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