Why is regularization important in AI training?

Why is regularization important in AI training?

Regularization helps prevent overfitting by adding a penalty for complexity in the model, ensuring better generalization.

Can you explain how regularization techniques like L1 and L2 work?

Sure! L1 regularization adds a penalty equal to the absolute value of the coefficients, promoting sparsity. L2 regularization adds a penalty equal to the square of the coefficients, which helps in reducing model complexity.

What are the consequences of not using regularization?

Without regularization, models may fit the training data too closely, leading to poor performance on unseen data due to overfitting.

Are there specific scenarios where regularization is not needed?

Yes, in cases where the dataset is large and the model is simple, regularization may not be necessary as the risk of overfitting is lower.