Neural Networks: Regression and Classification

This lecture covers the fundamentals of neural networks, focusing on regression and classification tasks. Topics include the structure of multilayer perceptrons, training with gradient descent, regression with MLPs, classification with MLPs, regularization techniques, and the flexibility of neural networks. Practical examples such as solving the XOR problem and fitting weather data are discussed, along with the application of neural networks in predicting complex densities. The lecture also explores the use of MLPs for regression and classification tasks, emphasizing the importance of regularization and early stopping in training neural networks.