Lecture
This lecture covers the philosophy of regularization and control of 'flexibility' in artificial neural networks. Various easy-to-implement regularization methods are discussed, including modified loss functions, training base, and validation base. The instructor explains the importance of splitting the data into training and validation bases to prevent overfitting and demonstrates how to optimize parameters using gradient descent. Different regularization techniques such as penalty terms and weight decay are explored, along with their impact on the network's flexibility. Practical examples of curve fitting and controlling flexibility through regularization are presented, emphasizing the need to balance model complexity and generalization.