Nonlinear Supervised LearningExplores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Neural Networks: Multilayer LearningCovers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Deep Learning FundamentalsIntroduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Neural Networks for NLPCovers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Convolutional Neural NetworksIntroduces Convolutional Neural Networks (CNNs) for autonomous vehicles, covering architecture, applications, and regularization techniques.