Covers convolutional neural networks, filter operations, and their applications in signal processing and image analysis.
Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.
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 Convolutional Neural Networks (CNNs) for autonomous vehicles, covering architecture, applications, and regularization techniques.
Introduces Convolutional Neural Networks, covering fully connected layers, convolutions, pooling, PyTorch translations, and applications like hand pose estimation and tubularity estimation.
