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File Date Author Commit
VS2010 2013-11-30 Steffen Nissen Steffen Nissen [3d0769] added experimental support for multiple processors
bin 2013-11-30 Steffen Nissen Steffen Nissen [3d0769] added experimental support for multiple processors
cmake 2012-01-23 Nissen Steffen Nissen Steffen [9b4444] make install work on OSX and update install ver...
datasets 2012-01-21 Steffen Nissen Steffen Nissen [65d9c3] Added more examples
examples 2013-11-30 Steffen Nissen Steffen Nissen [3d0769] added experimental support for multiple processors
lib 2012-06-17 Steffen Nissen Steffen Nissen [e7880e] Added unittests (for now only a few tests and o...
src 2013-11-30 Steffen Nissen Steffen Nissen [3d0769] added experimental support for multiple processors
tests 2012-10-28 Steffen Nissen Steffen Nissen [201ba5] added another function for setting training data
.gitignore 2012-01-23 Steffen Nissen Steffen Nissen [7a5800] updated binary files
CMakeLists.txt 2012-01-26 Steffen Nissen Steffen Nissen [f91582] Merge branch 'master' of ssh://fann.git.sourcef...
COPYING.txt 2012-01-26 Steffen Nissen Steffen Nissen [f91582] Merge branch 'master' of ssh://fann.git.sourcef...
README.txt 2012-01-23 Steffen Nissen Steffen Nissen [64a24b] file cleanup

Read Me

Fast Artificial Neural Network Library (FANN)

Fast Artificial Neural Network Library 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. Bindings to more than 15 programming
languages are available. An easy to read introduction article and a reference 
manual accompanies the library with examples and recommendations on how to use 
the library. Several graphical user interfaces are also available for the 
library.

FANN Features:
* Multilayer Artificial Neural Network Library in C
* Backpropagation training (RPROP, Quickprop, Batch, Incremental)
* Evolving topology training which dynamically builds and trains the ANN (Cascade2)
* Easy to use (create, train and run an ANN with just three function calls)
* Fast (up to 150 times faster execution than other libraries)
* Versatile (possible to adjust many parameters and features on-the-fly)
* Well documented (An easy to read introduction article, a thorough reference manual, and a 50+ page university report describing the implementation considerations etc.)
* Cross-platform (configure script for linux and unix, dll files for windows, project files for MSVC++ and Borland compilers are also reported to work)
* Several different activation functions implemented (including stepwise linear functions for that extra bit of speed)
* Easy to save and load entire ANNs
* Several easy to use examples
* Can use both floating point and fixed point numbers (actually both float, double and int are available)
* Cache optimized (for that extra bit of speed)
* Open source, but can still be used in commercial applications (licenced under LGPL)
* Framework for easy handling of training data sets
* Graphical Interfaces
* Language Bindings to a large number of different programming languages
* Widely used (approximately 100 downloads a day)

To get started with FANN, go to the FANN help site (http://leenissen.dk/fann/wp/help/), which will include links to all the available resources. 

For more information about FANN, please refer to the FANN website: 
http://leenissen.dk/fann/wp/