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Deep Belief Nets in C++ and CUDA C: Volume 1: Restricted Boltzmann Machines and Supervised Feedforward Networks by Timothy Masters

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© Timothy Masters 2018

Timothy Masters, Deep Belief Nets in C++ and CUDA C: Volume 1, https://doi.org/10.1007/978-1-4842-3591-1_2

2. Supervised Feedforward Networks

Timothy Masters

(1)Ithaca, New York, USA

Deep belief nets are generally trained in stages. First, one or more (usually more) layers are trained with unsupervised algorithms. Rather than seeking to learn class memberships or predicted values, the model simply tries to find consistent patterns within the independent variables. Only after such patterns have been found does training switch to supervised mode. However, because supervised training algorithms are easier to understand than the usual unsupervised algorithms, we will begin this study of deep belief nets with supervised training. ...

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