How does the learning rate affect model training?

How does the learning rate affect model training?

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 show me a graph that visualizes this effect?

Sure! A typical graph would show the training loss over epochs for different learning rates. You'd see that a high learning rate might cause the loss to fluctuate wildly, while a lower learning rate would show a smoother decline.

What are some common values for learning rates?

Common values for learning rates are often in the range of 0.001 to 0.1. However, the best value can vary depending on the specific model and dataset.

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

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