What is the importance of validation in deep learning?

Why is validation important in deep learning?

Validation is crucial in deep learning as it helps assess the model's performance on unseen data, preventing overfitting.

What happens if I skip the validation step?

Skipping validation can lead to a model that performs well on training data but poorly on new, unseen data.

How do I perform validation in my deep learning model?

You can perform validation by splitting your dataset into training, validation, and test sets, using techniques like k-fold cross-validation.

Are there different types of validation techniques?

Yes, common techniques include k-fold cross-validation, stratified sampling, and holdout validation.