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Explains the learning process in multi-layer neural networks, including back-propagation, activation functions, weights update, and error backpropagation.
Delves into deep learning's dimensionality, data representation, and performance in classifying large-dimensional data, exploring the curse of dimensionality and the neural tangent kernel.
Explores enhancing machine learning predictions by refining error metrics and applying constraints for improved accuracy in electron density predictions.
Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.