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Why should the learning rate be chosen carefully?

Answer and explanation

Correct answer: Because it affects the speed of model updates and the stability of training

The learning rate determines the size of the change made to the model’s weights at each update. A learning rate that is too high can overshoot the minimum-error point and make training unstable, while one that is too low makes training very slow. Therefore, it must balance learning speed and stability. Option B concerns only changing a name and has no role in model optimization.

Related tags

Learning RateNeural NetworksModel TrainingOptimizationGradient Descent

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