Difference between revisions of "Gneuralnetwork"
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The network can now learn tasks defined by the user. An example of script defining a simple network which fits a curve is given. We plan to deliver more advanced features very soon. In particular, we are already spending efforts to implement recurrent networks. We also plan to implement learning reinforcement techniques and apply Gneural Network for deep learning applications. We will release the data along with the trained network. | The network can now learn tasks defined by the user. An example of script defining a simple network which fits a curve is given. We plan to deliver more advanced features very soon. In particular, we are already spending efforts to implement recurrent networks. We also plan to implement learning reinforcement techniques and apply Gneural Network for deep learning applications. We will release the data along with the trained network. | ||
− | |Homepage URL= | + | |Homepage URL=https://www.gnu.org/software/gneuralnetwork/ |
|User level=advanced | |User level=advanced | ||
+ | |Is High Priority Project=No | ||
|Component programs=gneural | |Component programs=gneural | ||
+ | |Accepts cryptocurrency donations=No | ||
|Version identifier=0.9.1 | |Version identifier=0.9.1 | ||
|Version status=developmental | |Version status=developmental | ||
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|Submitted by=Quark | |Submitted by=Quark | ||
|Submitted date=2016/09/29 | |Submitted date=2016/09/29 | ||
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|Is GNU=Yes | |Is GNU=Yes | ||
|GNU package identifier=gneuralnetwork | |GNU package identifier=gneuralnetwork | ||
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|Role=Maintainer | |Role=Maintainer | ||
|Email=jeanmichel.sellier@gmail.com | |Email=jeanmichel.sellier@gmail.com | ||
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{{Software category | {{Software category |
Revision as of 19:15, 28 March 2018
GNU Gneural Network
https://www.gnu.org/software/gneuralnetwork/
Implement a programmable neural network.
Gneural Network is the GNU package which implements a programmable neural network. The current version, 0.9.1, has the following features:
- A scripting language is available which allows users to define their own neural network without having to know anything about coding.
- Advanced programmers can use the methods/routines inside the code for their own purposes.
- When defining the neurons of a network, it is possible to choose among various discriminant and activation functions, etc.
- Different methods to train a neural network are available, such as genetic algorithms, multi-scale Monte Carlo optimizers, simulated annealing, and others.
- Several training methods can run in parallel on clusters.
- Neural networks can be saved once trained for later use.
- The code is truly cross platform since it is entirely developed in C and does not depend on any external library.
The network can now learn tasks defined by the user. An example of script defining a simple network which fits a curve is given. We plan to deliver more advanced features very soon. In particular, we are already spending efforts to implement recurrent networks. We also plan to implement learning reinforcement techniques and apply Gneural Network for deep learning applications. We will release the data along with the trained network.
Licensing
License
Verified by
Verified on
Notes
Leaders and contributors
Contact(s) | Role |
---|---|
Ray Dillinger | contributor |
Jean Michel Sellier | Maintainer |
Aljosha Papsch | contributor |
Resources and communication
Software prerequisites
Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the page “GNU Free Documentation License”.
The copyright and license notices on this page only apply to the text on this page. Any software or copyright-licenses or other similar notices described in this text has its own copyright notice and license, which can usually be found in the distribution or license text itself.