Abstract
Recurrent neural networks (RNNs) are models that were created to tackle problems within the scope of pattern recognition and are fundamentally built on the same concepts with respect to feed-forward MLPs. The difference is that although MLPs by definition have multiple layers, RNNs do not and instead have a directed cycle through which the inputs are transformed into outputs. I’ll begin the chapter by covering several RNN models and end it with a practical application of RNNs.
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© 2017 Taweh Beysolow II
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Beysolow II, T. (2017). Recurrent Neural Networks (RNNs). In: Introduction to Deep Learning Using R. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-2734-3_6
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DOI: https://doi.org/10.1007/978-1-4842-2734-3_6
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-2733-6
Online ISBN: 978-1-4842-2734-3
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