Explores neural networks' ability to learn features and make linear predictions, emphasizing the importance of data quantity for effective performance.
Explains the learning process in multi-layer neural networks, including back-propagation, activation functions, weights update, and error backpropagation.
Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.