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Neural Network Learning by the Levenberg-Marquardt Algorithm with Bayesian Regularization (part 1)

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25 Feb 2010CPOL7 min read 76.3K   2K   45  
A complete explanation for the totally lost, part 1 of 2. The Levenberg–Marquardt algorithm provides a numerical solution to the problem of minimizing a (generally nonlinear) function. This article shows how the Levenberg-Marquart can be used to train Neural Networks.

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This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)


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Engineer NAVER LABS Europe
France France
Computer and technology enthusiast, interested in artificial intelligence and image processing. Has a Master's degree on Computer Science specialized on Image and Signal Processing, with expertise on Machine Learning, Computer Vision, Pattern Recognition and Data Mining systems. Author of the Accord.NET Framework for developing scientific computing applications.

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