Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.
