Quantum Many-body Dynamics with Artificial Neural Networks

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Page 3 of 4

Introduces Convolutional Neural Networks (CNNs) for autonomous vehicles, covering architecture, applications, and regularization techniques.

Machine Learning Basics

Covers machine learning basics, neural networks, CNNs, and model tuning.

Environmental Noise Impact Studies

Explores environmental noise, its characteristics, impact on health, measurement methods, and legal regulations.

Regularization by Early Stopping

Explores regularization by early stopping in deep neural networks to control flexibility and avoid overfitting.

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.

Convolutional Neural Networks

Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.

Deep Learning: Data Representations and Neural Networks

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Feedforward Neural Networks: Activation Functions and Backpropagation

Introduces feedforward neural networks, activation functions, and backpropagation for training, addressing challenges and powerful methods.

NFNets: Removing BatchNorm for High-Performance Image Recognition

Explores NFNets as an alternative to BatchNorm in ResNets, achieving high performance on ImageNet.

Environmental Impact Studies: Noise Pollution and Mitigation Measures

Explores environmental impact studies on noise pollution assessment and mitigation measures in various settings.