47 lines
2.3 KiB
Plaintext
47 lines
2.3 KiB
Plaintext
The MIT License (MIT)
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Copyright (c) 2017 Juan Cazala - https://caza.la
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in
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all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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THE SOFTWARE
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********************************************************************************************
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SYNAPTIC (v1.1.4)
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********************************************************************************************
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Synaptic is a javascript neural network library for node.js and the browser, its generalized
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algorithm is architecture-free, so you can build and train basically any type of first order
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or even second order neural network architectures.
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http://en.wikipedia.org/wiki/Recurrent_neural_network#Second_Order_Recurrent_Neural_Network
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The library includes a few built-in architectures like multilayer perceptrons, multilayer
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long-short term memory networks (LSTM) or liquid state machines, and a trainer capable of
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training any given network, and includes built-in training tasks/tests like solving an XOR,
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passing a Distracted Sequence Recall test or an Embeded Reber Grammar test.
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The algorithm implemented by this library has been taken from Derek D. Monner's paper:
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A generalized LSTM-like training algorithm for second-order recurrent neural networks
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http://www.overcomplete.net/papers/nn2012.pdf
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There are references to the equations in that paper commented through the source code.
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