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Recurrent Neural Networks

  • Ovidiu CalinEmail author
Chapter
  • 91 Downloads
Part of the Springer Series in the Data Sciences book series (SSDS)

Abstract

A feedforward fully-connected neural network cannot be used successfully for modeling sequences of data. A few basic reasons are the following: they cannot handle variable-length input sequences, do not share parameters, cannot track long-term dependencies, and cannot maintain information about the order of input data. Hence there is a need for a neural architecture that can handle successfully all the previous requirements.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Mathematics & StatisticsEastern Michigan UniversityYpsilantiUSA

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