NuGet Gallery Feed for FANNCSharp-x86Fast 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.
Fann C# is a wapper around FANN that lets you use the FANN libraries from C# on Windows. Currently all methods of the neural_net and training_data classes have been implemented. Additionally the new FANN parallel methods have been added as part of the NeuralNet classes.https://www.nuget.org/packages/FANNCSharp-x86/2018-12-09T12:42:40Zhttps://api.nuget.org/v3-flatcontainer/fanncsharp-x86/0.1.8/iconhttps://www.nuget.org/packages/FANNCSharp-x86/0.1.8FANNCSharp-x86 0.1.82016-04-08T02:34:16Z2018-12-09T12:42:40Zjoelselfhttps://www.nuget.org/profiles/joelselfFast 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.
Fann C# is a wapper around FANN that lets you use the FANN libraries from C# on Windows. Currently all methods of the neural_net and training_data classes have been implemented. Additionally the new FANN parallel methods have been added as part of the NeuralNet classes.https://www.nuget.org/packages/FANNCSharp-x86/0.1.7FANNCSharp-x86 0.1.72016-02-08T00:53:01Z2018-12-09T12:42:33Zjoelselfhttps://www.nuget.org/profiles/joelselfFast 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.
Fann C# is a wapper around FANN that lets you use the FANN libraries from C# on Windows. Currently all methods of the neural_net and training_data classes have been implemented. Additionally the new FANN parallel methods have been added as part of the NeuralNet classes.https://www.nuget.org/packages/FANNCSharp-x86/0.1.4FANNCSharp-x86 0.1.42016-04-08T00:58:33Z2018-12-09T12:42:33Zjoelselfhttps://www.nuget.org/profiles/joelselfFast 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.
Fann C# is a wapper around FANN that lets you use the FANN libraries from C# on Windows. Currently all methods of the neural_net and training_data classes have been implemented. Additionally the new FANN parallel methods have been added as part of the NeuralNet classes.https://www.nuget.org/packages/FANNCSharp-x86/0.1.3FANNCSharp-x86 0.1.32016-01-09T01:12:37Z2018-12-09T12:42:39Zjoelselfhttps://www.nuget.org/profiles/joelselfFast 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.
Fann C# is a wapper around FANN that lets you use the FANN libraries from C# on Windows. Currently all methods of the neural_net and training_data classes have been implemented. Additionally the new FANN parallel methods have been added as part of the NeuralNet classes.