What is the impact of learning rate on model performance?

What is the impact of learning rate on model performance?

The learning rate determines how much to change the model in response to the estimated error each time the model weights are updated. A high learning rate can lead to faster convergence but may overshoot the optimal solution, while a low learning rate ensures more precise updates but can slow down the training process.

Can you explain how to choose the right learning rate?

Choosing the right learning rate often involves experimentation. You can start with a small value and gradually increase it, or use techniques like learning rate schedules or adaptive learning rate methods like Adam.

What are the consequences of a poorly chosen learning rate?

A poorly chosen learning rate can lead to slow convergence, getting stuck in local minima, or even divergence where the model fails to learn altogether.

Are there any tools to help find the optimal learning rate?

Yes, tools like learning rate finders can help. They typically plot the loss against different learning rates to visualize the best rate for your model.