What are the best practices for using optimizers in machine learning?

What are the best practices for using optimizers in machine learning?

When using optimizers in machine learning, it's important to choose the right optimizer for your model, tune hyperparameters, and monitor training closely.

Can you explain how to choose the right optimizer?

Choosing the right optimizer depends on your specific problem. For example, Adam is great for most tasks, while SGD can be better for large datasets.

What hyperparameters should I tune for optimizers?

Key hyperparameters to tune include the learning rate, momentum, and decay rates. These can significantly impact your model's performance.

How can I monitor the training process effectively?

You can monitor training by tracking loss and accuracy metrics, using validation sets, and visualizing results with tools like TensorBoard.