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Neural Networks Tools  
List of neural networks software tools from Makhfi.com. This site also contains links to neural networks resources, news and introductions focused on neural networks.
Submitted: Nov 10, 2003
Neural Network Source Code  
A basic back propagation algorithm written in C++.
Submitted: Aug 07, 2005
c/c++ Neural Network backprop code  
Very simple neural network backprop code.
Submitted: Feb 12, 2006
NeuroSolutions is a highly graphical and flexible neural network development tool for Windows. Neural netowrk models created with NeuroSolutions can be embeded into other applications by using the C++ source code generation feature.
Submitted: Nov 11, 2001
Torch Library  
Torch is a machine learning library written in C++ that works on most Unix/Linux platforms. It can be used to train MLPs, RBFs, HMMs, Gaussian Mixtures, Kmeans, Mixtures of experts, Parzen Windows, KNN, and can be easily extended so that you can add your own machine learning algorithms.
Submitted: Aug 02, 2002
TNN library  
Toroidal neural networks are a particular kind of DT-CNN (discrete time cellular neural networks) often used for images manipulation. They have got an underlying toroidal topology and this reflects in the use of circulating matrices to describe their cofigurations. The TNNlib provides a set of instruments ideated to simplify the software implementation of these structures. This C++ library features a completely general, template based, matrix class (matrix.h), and some specific TNN routines (tnn.h). It has been widely tested either on linux (red hat 7.2) and windows 98 (djgpp).
CONICAL (Computational Neuroscience Class Library) is a C++ class library for building simulations common in computational neuroscience. From this site you can download the documentation for that library and source code.
Submitted: Nov 14, 1999
SNNS (Stuttgart Neural Network Simulator) is a software simulator for neural networks on Unix workstations developed at the Institute for Parallel and Distributed High Performance Systems (IPVR) at the University of Stuttgart. The goal of the SNNS project is to create an efficient and flexible simulation environment for research on and application of neural nets. The SNNS simulator consists of two main components: 1) simulator kernel written in C 2) graphical user interface under X11R4 or X11R5.
Submitted: Nov 13, 1999

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