Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Covers convolutional neural networks, filter operations, and their applications in signal processing and image analysis.
Introduces Convolutional Neural Networks (CNNs) for autonomous vehicles, covering architecture, applications, and regularization techniques.
Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Explores convolutional neural networks, covering convolution, cross-correlation, max pooling, layer structure, and examples like LeNet5 and AlexNet.
