What is the impact of learning rate in machine learning?

What is the impact of learning rate in machine learning?

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

How does a high learning rate affect training?

A high learning rate can cause the model to converge too quickly to a suboptimal solution or even diverge.

What about a low learning rate?

A low learning rate can lead to a more precise convergence but may take a long time to train and risk getting stuck in local minima.

How can I choose the right learning rate?

You can experiment with different learning rates, use techniques like learning rate schedules, or employ methods like grid search.

Are there any tools to help with this?

Yes, libraries like TensorFlow and PyTorch offer built-in functions to adjust and optimize learning rates during training.