Fast Artificial Neural Network Library 2.1.0 Beta

Fast Artificial Neural Network Library 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, Python, Delphi and Mathematica bindings are available.
Main 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 use reference manual, a 50+ page university report describing the implementation considerations etc. and an introduction article)
- 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 (simple train example and simple test example)
- 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 (licenced under LGPL)
- Framework for easy handling of training data sets
- Graphical Interface
- C++ Bindings
- PHP Extension
- Python Bindings
- Delphi Bindings
- .NET Bindings
- Mathematica Extension
- Octave Extension
- Ruby Bindings
- Pure Data Bindings
- Debian package