Deep Learning: Data Representations and Neural Networks
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Explores the importance of causality for robust machine learning, covering ideal datasets, missing data problems, graphical models, and interference models.
Discusses minima in error functions, multiple minima, saddle points, weight space symmetry, and near-equivalent good solutions in deep neural networks.
Explores the evolution of vision theories and visual feedback systems, including bottom-up and top-down approaches, and iterative error feedback for human pose estimation.
Explores emotion theories, applications, and predictive models in affective computing, analyzing NSF funding trends, emotion impact on education and medicine, and emotion detection through physiological signals and visual data.
Covers photonic extreme learning machines and reservoir computing, focusing on their architectures, programming techniques, and applications in optical computing.
