Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.
Explores the regularized cross-entropy risk in neural networks, covering training processes and challenges in deep networks.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
