Training Large Neural Networks
نویسنده
چکیده
We describe regularization tools for training large-scale artiicial feed-forward neural networks. We propose algorithms that explicitly use a sequence of Tikhonov regularized nonlinear least squares problems. For large-scale problems, methods using new special purpose automatic diierentiation are used in a conjugate gradient method for computing a truncated Gauss-Newton search direction. The algorithms developed utilize the structure of the problem in diierent ways and perform much better than a Polak-Ribi ere based method. All algorithms are tested using benchmark problems and guidelines by Lutz Prechelt in the Proben1 package. All software is written in Matlab and gathered in a toolbox.
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