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An overview of a neural network model using sigmoid and rectified linear units (ReLUs). It includes the architecture of the network, the calculation of errors using binary cross entropy, squared error, and sparse cross entropy, and the implementation of ReLUs to mitigate the vanishing gradient problem.
Typology: Lecture notes
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