What is the impact of learning rate on different optimizers?

What is the impact of learning rate on different optimizers?

The learning rate is crucial as it determines how much to change the model in response to the estimated error each time the model weights are updated. Different optimizers handle learning rates differently.

Can you explain how Adam optimizer uses learning rate?

Sure! The Adam optimizer adapts the learning rate for each parameter individually, which helps in faster convergence and better performance.

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 poor performance.

And what if it's too low?

A learning rate that is too low can result in a very slow convergence, making the training process inefficient and time-consuming.

How can I choose the right learning rate for my model?

You can use techniques like learning rate schedules, grid search, or adaptive learning rate methods to find the optimal learning rate for your model.