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.
Covers hand pose estimation, regression techniques, and the evolution of image classification models from LeNet to VGG19.
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, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
