What are the best practices for optimizer tuning in machine learning?

What are the best practices for optimizer tuning in machine learning?

Some best practices include starting with a well-known optimizer, adjusting learning rates, and using grid search for hyperparameters.

How do I choose the right learning rate?

Choosing the right learning rate often involves experimentation. Start with a small value and gradually increase it while monitoring performance.

What is the impact of batch size on optimizer performance?

Batch size can significantly affect training speed and model performance. Smaller batches provide more updates but can be noisy, while larger batches stabilize the updates.

Can you explain the difference between SGD and Adam optimizers?

SGD (Stochastic Gradient Descent) updates parameters based on the average of gradients, while Adam adapts the learning rate for each parameter based on first and second moments of the gradients.