Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
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.
Explores the regularized cross-entropy risk in neural networks, covering training processes and challenges in deep networks.
