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.
Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.
Offers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.
