What are the common hyperparameters in machine learning?

What are the common hyperparameters in machine learning?

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

Can you explain what learning rate does?

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?

If the learning rate is too high, the model may converge too quickly to a suboptimal solution or even diverge.

And what about batch size?

Batch size determines the number of training examples utilized in one iteration. Smaller batches provide a more accurate estimate of the gradient.