What are common hyperparameters in machine learning?

What are common hyperparameters in machine learning?

Common hyperparameters include learning rate, batch size, number of epochs, and regularization parameters.

Can you explain the learning rate and its importance?

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

What happens if the learning rate is too high or too low?

If it's too high, the model may converge too quickly to a suboptimal solution. If it's too low, training can be very slow and may get stuck.

Are there any techniques to find the best hyperparameters?

Yes, techniques like grid search, random search, and Bayesian optimization can help find the best hyperparameters.