Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.
Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
