Explores the deep learning paradigm, including challenges, neural networks, robustness, fairness, interpretability, and energy efficiency.
Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
