Official github repository for Fast Artificial Neural Network Library (FANN)
C++
1,749
210 commits
updated Jul 6, 2026
Fast Artificial Neural Network (FANN) 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.
First you'll want to clone the repository:
git clone https://github.com/libfann/fann.git
Once that's finished, navigate to the Root directory. In this case it would be ./fann:
cd ./fann
Then run CMake
cmake .
After that, you'll need to use elevated privileges to install the library:
sudo make install
That's it! If everything went right, you should see a lot of text, and FANN should be installed!
Many Linux distributions carry precompiled FANN packages in their binary repositories.
See Repology for further information: .
On Debian derived distributions install the package libfann2 and potentially libfann-dev if you desire to develop for the library. Fx. by issuing the following command in a root terminal:
apt install libfann2 libfann-dev
You can download and install fann using the vcpkg dependency manager:
git clone https://github.com/Microsoft/vcpkg.git
cd vcpkg
./bootstrap-vcpkg.sh
./vcpkg integrate install
./vcpkg install fann
The fann port in vcpkg is kept up to date by Microsoft team members and community contributors. If the version is out of date, please create an issue or pull request on the vcpkg repository.
478 followers · starred May 2014
424 followers · starred Jun 2015
106 followers · starred Jul 2023
5,558 followers · starred Nov 2022
Official github repository for Fast Artificial Neural Network Library (FANN)
C++
1,749
210 commits
updated Jul 6, 2026
Fast Artificial Neural Network (FANN) 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.
First you'll want to clone the repository:
git clone https://github.com/libfann/fann.git
Once that's finished, navigate to the Root directory. In this case it would be ./fann:
cd ./fann
Then run CMake
cmake .
After that, you'll need to use elevated privileges to install the library:
sudo make install
That's it! If everything went right, you should see a lot of text, and FANN should be installed!
Many Linux distributions carry precompiled FANN packages in their binary repositories.
See Repology for further information: .
On Debian derived distributions install the package libfann2 and potentially libfann-dev if you desire to develop for the library. Fx. by issuing the following command in a root terminal:
apt install libfann2 libfann-dev
You can download and install fann using the vcpkg dependency manager:
git clone https://github.com/Microsoft/vcpkg.git
cd vcpkg
./bootstrap-vcpkg.sh
./vcpkg integrate install
./vcpkg install fann
The fann port in vcpkg is kept up to date by Microsoft team members and community contributors. If the version is out of date, please create an issue or pull request on the vcpkg repository.
478 followers · starred May 2014
424 followers · starred Jun 2015
106 followers · starred Jul 2023
5,558 followers · starred Nov 2022