Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.
