How can I optimize hyperparameters in gradient descent?

How can I optimize hyperparameters in gradient descent?

To optimize hyperparameters in gradient descent, you can use techniques like grid search, random search, or Bayesian optimization.

What are the most important hyperparameters to tune?

The most important hyperparameters include the learning rate, batch size, and the number of epochs.

How do I choose the right learning rate?

You can start with a small learning rate and gradually increase it, or use techniques like learning rate schedules or adaptive learning rates.

Can you explain grid search in more detail?

Grid search involves defining a grid of hyperparameter values and evaluating the model's performance for each combination.