Topic 03: Regularization

This chapter introduces and discusses regularization techniques for neural networks, which help prevent overfitting and improve generalization. It provides an introduction and geometric intuition of L2-regularization. Additionally, it introduces dropout, a method of randomly deactivating neurons during training to enhance robustness, and early stopping, which monitors validation performance to halt training when overfitting begins.