A Diagrammatic Approach to Gradient Derivations for Neural Networks
نویسنده
چکیده
Deriving gradient algorithms for time-dependent neural network structures typically requires numerous chain rule expansions, diligent bookkeeping, and careful manipulation of terms. We show, however, that an eecient gradient descent algorithm may be formulated for any network structure with virtually no eeort using a set of simple block diagram manipulation rules. Examples are provided that illustrate the simplicity of the approach for a variety of structures, including feedforward and feedback systems.
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