Multiple Choice Question

Learning rate is a key hyperparameter in training neural networks and other models.

What is the purpose of a "learning rate" in gradient descent optimization?

A

To determine the size of steps taken towards the minimum of a function
B

To increase the model's complexity
C

To define the number of iterations
D

To prevent the model from underfitting
E

To ensure the model converges to a local maximum

Explanation:

The learning rate controls how much to change the model in response to the estimated error each time the model weights are updated.

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