What is hyperparameter tuning and why is it important?

What is hyperparameter tuning and why is it important?

Hyperparameter tuning is the process of optimizing the parameters that govern the training of a machine learning model. It's crucial because it can significantly affect the model's performance.

Can you give examples of hyperparameters in different domains?

Sure! In image classification, hyperparameters might include the learning rate and batch size. In natural language processing, they could be the number of layers in a neural network or the dropout rate.

How do I choose the right hyperparameters for my model?

You can use techniques like grid search, random search, or Bayesian optimization to systematically explore different hyperparameter combinations and find the best ones.

Are there any tools that can help with hyperparameter tuning?

Yes, there are several tools available, such as Optuna, Hyperopt, and Google Cloud's AI Platform, which can automate the tuning process.