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With the release of version 0.3.0 in April 2016 [4] the use in production and research environments became more widespread. The package was reviewed several months later on the R blog The Beginner Programmer as "R provides a simple and very user friendly package named rnn for working with recurrent neural networks.", [5] which further increased usage.
A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topological order.
Windows 7, Windows Server 2008 R2, Windows 8, Windows Server 2012, Windows 8.1, Windows Server 2012 R2[4], Windows 10, Windows Server 2016 Yes, Yes Proprietary: March 7, 2017 XPEDITER: 1980? family of mainframe debuggers COBOL, PL/1 & Assembler: z/OS: Yes Yes Proprietary: z2.1, Oct 2014
At the input level, it learns to predict its next input from the previous inputs. Only unpredictable inputs of some RNN in the hierarchy become inputs to the next higher level RNN, which therefore recomputes its internal state only rarely. Each higher level RNN thus studies a compressed representation of the information in the RNN below.
Written in Java only Windows Linux macOS Other platforms GUI builder Profiling RDBMS EE Limitations BlueJ: GPL2+GNU linking exception: No Yes Yes Yes Yes Solaris: No Not a General IDE; a small scale UML editor DrJava: Permissive: No Yes Yes Yes Yes Solaris: No Java 8 only (2014) Eclipse JDT: EPL: Yes No [40] Yes Yes Yes FreeBSD, JVM, Solaris ...
Language Original purpose Imperative Object-oriented Functional Procedural Generic Reflective Other paradigms Standardized; 1C:Enterprise programming language: Application, RAD, business, general, web, mobile: Yes No Yes Yes Yes Yes Object-based, Prototype-based programming No ActionScript: Application, client-side, web Yes Yes Yes Yes No No ...
A RNN (often a LSTM) where a series is decomposed into a number of scales where every scale informs the primary length between two consecutive points. A first order scale consists of a normal RNN, a second order consists of all points separated by two indices and so on. The Nth order RNN connects the first and last node.
Structure of RNN and BRNN [1] The principle of BRNN is to split the neurons of a regular RNN into two directions, one for positive time direction (forward states), and another for negative time direction (backward states). Those two states' output are not connected to inputs of the opposite direction states.