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.

How do I choose the right learning rate?

Choosing the right learning rate can be done through experimentation. You can start with a small value and gradually increase it, or use techniques like learning rate schedules.

What happens if the learning rate is too high?

If the learning rate is too high, the model may converge too quickly to a suboptimal solution or even diverge, leading to erratic behavior.

Can you explain learning rate decay?

Learning rate decay is a technique where the learning rate decreases over time, allowing for more fine-tuned adjustments as the model approaches convergence.