Parameter or hyperparameter?

In the previous video, you learned about hyperparameters in machine learning and the importance of tuning them. Automating this optimization is key, but it's important to distinguish between hyperparameters and model parameters, as they play different roles in the model development process. Hyperparameters are set before training, while model parameters are learned during training. The way we automate finding the best hyperparameters is different from how we find model parameters, making it important to distinguish between them.

This exercise will test your ability to recognize examples of commonly used hyperparameters in ML systems.