How do I tune parameters for data augmentation and regularization?

How do I tune parameters for data augmentation and regularization?

Tuning parameters for data augmentation and regularization involves adjusting settings to improve model performance. What specific techniques are you using?

I'm using image data augmentation techniques like rotation and flipping. What parameters should I focus on?

For image augmentation, focus on the degree of rotation, the probability of applying each transformation, and the size of the augmented images. Would you like to know more about regularization techniques?

Yes, what regularization methods should I consider?

Common regularization methods include L1 and L2 regularization, dropout, and early stopping. Each has its own parameters to tune. Do you have a specific method in mind?

I'm interested in dropout. How do I set the dropout rate?

The dropout rate typically ranges from 0.2 to 0.5. Start with 0.5 and adjust based on validation performance. Would you like tips on monitoring this?