Recurrent Neural Networks: Design and Applications Lakhmi C. Jain Larry Medsker With existent uses ranging from motion detection to music synthesis to financial forecasting, recurrent neural networks have generated widespread attention. The tremendo
Recurrent Neural Networks for Prediction offers a new insight into the learning algorithms, architectures and stability of recurrent neural networks and, consequently, will have instant appeal. It provides an extensive background for researchers, ac
eep Learning: Recurrent Neural Networks in Python: LSTM, GRU, and more RNN machine learning architectures in Python and Theano (Machine Learning in Python) by LazyProgrammer English | 8 Aug 2016 | ASIN: B01K31SQQA | 86 Pages | AZW3/MOBI/EPUB/PDF (co
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Abstract: This tutorial is a worked-out version of a 5-hour course originally held at AIS in September/October 2002. It has two distinct components. First, it contains a mathematically-oriented crash course on traditional training methods for recurr
• Why Recurrent Neural Networks (RNNs)?? • The Vanilla RNN unit? • The RNN forward pass? • Backpropagation refresher? • The RNN backward pass? • Issues with the Vanilla RNN? • The Long Short-Term Memory (LSTM) unit? • The LSTM Forward & Backward pas
Abstract—This paper studies the approximation ability of con- tinuous-time recurrent neural networksto dynamical time-variant systems. It proves that any finite time trajectory of a given dynam- ical time-variant system can be approximated by the in
James Martens JMARTENS @ CS . TORONTO . EDU Ilya Sutskever ILYA @ CS . UTORONTO . CA University of Toronto, Canada Abstract In this work we resolve the long-outstanding problem of how to effectively train recurrent neu- ral networks (RNNs) on comple
Abstract Time series often have a temporal hierarchy, with information that is spread out over multiple time scales. Common recurrent neural networks, however, do not explicitly accommodate such a hierarchy, and most research on them has been focusi
