How do I tune hyperparameters for gradient descent?

How do I tune hyperparameters for gradient descent?

Tuning hyperparameters for gradient descent involves adjusting parameters like learning rate, batch size, and momentum. Would you like to know more about a specific parameter?

Yes, what is the best way to choose a learning rate?

A common approach is to start with a small learning rate and gradually increase it. You can also use techniques like learning rate schedules or adaptive learning rates.

What are learning rate schedules?

Learning rate schedules adjust the learning rate during training. Common types include step decay, exponential decay, and cyclical learning rates.

How do I implement a learning rate schedule in my code?

You can implement it using libraries like TensorFlow or PyTorch, which have built-in functions for various learning rate schedules. Would you like a code example?