What is the impact of learning rate on training a neural network?

What is the impact of learning rate on training a neural network?

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 what happens if the learning rate is too high?

If the learning rate is too high, the model may diverge instead of converging. This means that the loss function may increase rather than decrease, leading to poor performance and instability during training.

What about if the learning rate is too low?

A low learning rate can lead to very slow convergence, meaning the model takes a long time to reach the optimal solution. In some cases, it might get stuck in local minima and never reach the global minimum.

How can I determine the best learning rate for my model?

You can use techniques like learning rate schedules, grid search, or the learning rate finder method to experiment and find the optimal learning rate for your specific model and dataset.