What is the learning rate in gradient descent?

What is the learning rate in gradient descent?

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

Why is the learning rate important?

It's crucial because a learning rate that's too high can cause the model to converge too quickly to a suboptimal solution, while a rate that's too low can make the training process unnecessarily slow.

How do I choose the right learning rate?

You can start with common values like 0.01 or 0.001 and use techniques like learning rate schedules or adaptive learning rates to find the best fit.

What happens if I set the learning rate to zero?

If the learning rate is set to zero, the model will not learn at all, as there will be no updates to the weights during training.