What do you use?
views:
503answers:
4
+3
A:
Whenever I've wanted to play around with any data mining algorithm quickly, I just load up Weka. It's pretty complex but it implements a lot of algorithms (including neural networks) with a lot of customizability. Plus, it has some visualizations for NNs.
Daniel Lew
2009-03-26 20:28:11
+2
A:
It is old, but I have always used NeuroShell 2 when not using my own code. Unfortunately, it is not free. I think The newer NeuroShells are designed only for predicting stocks.
Kevin Crowell
2009-03-26 20:33:02
+2
A:
There are a lot of different network simulators dependant on how detailed you want to do your sim, and what kind of network you want to simulate.
NEURON and GENESIS are good if you want to simulate full biological networks (Which I'm geussing you probably don't) even down to the behaviour of dendrites etc.
NEST and SPLIT and some others are good for doing population simulations where you create the population on a node-by-node basis and see what the whole population does. This is pretty much the 'industry' standard approach, and is used a lot in research and commercial applications, so there are worth looking into. I know that IBM use SPLIT for some of their research.
MIIND is good if you want to use differential equations to model what a population would do, but this approach is relatively new and computationally expensive (if very cool).
Not sure if that is exactly what you wanted!
(N.B. if you google any of the names in caps along with the word simulator you will end up at the relevant web page =)
Ed Woodcock
2009-04-07 16:33:06
I've used Neuron and Genesis etc. but I've never heard of SPLIT nor did google find it for me. Could you give me the link?
bias
2009-04-11 19:43:04
I'm afraid I've never actually used it: my main interest in these simulators is architecture-wise due to my work at Uni, so I'm not sure if it's in the public domain. However I found my information on it in the paper 'Brain-scale simulation of the neocortex on an IBM Blue Gene/L supercomputer' (IBM)
Ed Woodcock
2009-04-12 19:37:18
+1
A:
Fast Artificial Neural Network Library (FANN) is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. PHP, C++, .NET, Ada, Python, Delphi, Octave, Ruby, Prolog Pure Data and Mathematica bindings are available.
FannTool A graphical user interface is also available for the library.
bluekid
2010-01-04 13:41:14