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Week 2 Introduction to Deep Learning [slides] [video]
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Learn PDE-modeled physics from data with neural networks
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From McCulloch–Pitts and the perceptron to MLPs: affine maps, pointwise activations, and stacked layers
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Universal approximation: an MLP can represent a continuous map, but the parameters still have to be found
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Supervised learning: labeled data, training loss, and a validation set to check generalization
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Training by gradient descent, backpropagation, and mini-batch SGD
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Beyond dense MLPs: CNNs and other structured architectures for high-dimensional inputs
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