Feed-forward Networks

This lecture covers the fundamentals of feed-forward networks, including the structure of neural networks, training goals, activation functions, gradient descent optimization, backpropagation, and the challenges of vanishing and exploding gradients. It also explores the application of feed-forward networks in time series forecasting, volatility forecasting, and bond return predictability using machine learning techniques.