Neuroph for Windows 2.9
Neuroph is lightweight Java neural network framework to develop common neural network architectures. It contains well designed, open source Java library with small number of basic classes which correspond to basic NN concepts. Also has nice GUI neural network editor to quickly create Java neural network components. It has been released as open source under the Apache 2.0 license, and it's FREE for you to use it.
Neuroph simplifies the development of neural networks by providing Java neural network library and GUI tool that supports creating, training and saving neural networks.
If you are beginner with neural networks, and you just want to try how they work without going into complicated theory and implementation, or you need them quickly for your research project the Neuroph is good choice for you. It is small, well documented, easy to use, and very flexible neural network framework.
Creating a neural network is not something that can be done by any amateur with a computer. Fortunately, the procedure can be learned and Neuroph can make it quite easy. The software can be used to create common neural network architectures, using a library of classes for basic neural network concepts.
Here is a partial list of some important features of the Neuroph framework:
Supported neural network architectures
* Multi Layer Perceptron with Backpropagation, Momentum on Resilient Propagation
* Hopfield network
* Bidirectional Associative Memory
* Kohonen network
* Hebbian network
* Competitive network
* RBF network
* Neuro Fuzzy Reasoner
* Small number of the essential base classes in core package (only 10) which can be easily reused or extended
* Support for supervised and unsupervised learning rules
* An easy-to-follow structure and logic.
* Java & Neural Network IDE, Neuroph Studio, based on NetBeans Platform
* Image recognition support
* OCR support
* Stock market prediction sample
* Learning vizualisation samples
* Data normalization
* Simple microbenchmarking framework
|Installation||Instal And Uninstall|
|Keywords||neural network, neural network architectures, NN concept, neural, architecture|
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