What are some effective techniques for reducing error rates in AI models?

What are some effective techniques for reducing error rates in AI models?

There are several techniques to reduce error rates, including data augmentation, regularization, and hyperparameter tuning.

Can you explain how data augmentation works?

Data augmentation involves creating new training samples by applying transformations to existing data, like rotation or scaling.

What is regularization and how does it help?

Regularization adds a penalty to the loss function to prevent overfitting, helping the model generalize better to unseen data.

How do I choose the right hyperparameters?

You can use techniques like grid search or random search to systematically explore different hyperparameter combinations.