Dropout: Tricks of the Trade

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Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Machine Learning for Solving PDEs: Random Feature Method

Explores the Random Feature Method for solving PDEs using machine learning algorithms to approximate high-dimensional functions efficiently.

Understanding Deep Learning

Explores deep learning fundamentals, including image classification, neural network working principles, and machine learning challenges.

Neural Networks: Deep Neural Networks

Explores the basics of neural networks, with a focus on deep neural networks and their architecture and training.

Neural Networks: Multilayer Learning

Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Splines and Imaging: From Compressed Sensing to Deep Neural Nets

Explores the optimality of splines for imaging and deep neural networks, demonstrating sparsity and global optimality with spline activations.

Multilayer Neural Networks: Deep Learning

Covers the fundamentals of multilayer neural networks and deep learning.

Neuroscience and AI: Bridging the Gap

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Deep Learning Agents: Reinforcement Learning

Explores Deep Learning Agents in Reinforcement Learning, emphasizing neural network approximations and challenges in training multiagent systems.