Convolutional Neural NetworksCovers convolutional neural networks, filter operations, and their applications in signal processing and image analysis.
Nonlinear Supervised LearningExplores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Convolutional Neural NetworksIntroduces Convolutional Neural Networks (CNNs) for autonomous vehicles, covering architecture, applications, and regularization techniques.
Convolutional Neural NetworksExplores convolutional neural networks, covering convolution, cross-correlation, max pooling, layer structure, and examples like LeNet5 and AlexNet.
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.
Deep Learning FundamentalsIntroduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Multi-layer Neural NetworksCovers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.