Titlebook: Deep Neural Networks in a Mathematical Framework; Anthony L. Caterini,Dong Eui Chang Book 2018 The Author(s) 2018 deep learning.machine le

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Deutschland 20 Jahre nach dem Mauerfall require when performing gradient descent steps to optimize the neural network. To represent the dependence of a neural network on its parameters, we then introduce the notion of parameter-dependent maps, including distinct notation for derivatives with respect to parameters as opposed to state vari

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Abschied vom Sozialstaat alter Pr?gungmeters, which allow us to perform gradient descent naturally over these vector spaces for each parameter. This approach contrasts with standard approaches to neural network modelling where the parameters are broken down into their components. We can avoid this unnecessary operation using the framewo

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Generic Representation of Neural Networks,meters, which allow us to perform gradient descent naturally over these vector spaces for each parameter. This approach contrasts with standard approaches to neural network modelling where the parameters are broken down into their components. We can avoid this unnecessary operation using the framewo

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