Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
