What is the difference between regularization and optimization in AI training?

What is the difference between regularization and optimization in AI training?

Regularization is a technique used to prevent overfitting by adding a penalty to the loss function, while optimization focuses on minimizing the loss function to improve model performance.

Can you give me examples of regularization techniques?

Sure! Common regularization techniques include L1 regularization (Lasso), L2 regularization (Ridge), and dropout.

How do these techniques affect model performance?

Regularization techniques help improve generalization by reducing overfitting, which can lead to better performance on unseen data.

What are some optimization algorithms used in AI?

Some popular optimization algorithms include Gradient Descent, Adam, and RMSprop, each with its own advantages for different scenarios.