Regularization by Early Stopping

This lecture covers the concept of regularization by early stopping in deep neural networks. The instructor explains how to minimize training error stepwise by gradient descent and control flexibility via learning time. The importance of going back to an earlier solution to avoid overfitting is highlighted, along with practical examples of the noisy XOR problem. The lecture also discusses the need for multiple layers in solving the XOR problem and the role of hidden neurons in adding flexibility while emphasizing the importance of controlling flexibility through hyperparameters or early stopping.