System.Numerics.Tensors 0.1.0

Tensor class which represents and extends multi-dimensional arrays.

Commonly Used Types:

There is a newer prerelease version of this package available.
See the version list below for details.

Requires NuGet 2.8.6 or higher.

Install-Package System.Numerics.Tensors -Version 0.1.0
dotnet add package System.Numerics.Tensors --version 0.1.0
<PackageReference Include="System.Numerics.Tensors" Version="0.1.0" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add System.Numerics.Tensors --version 0.1.0
The NuGet Team does not provide support for this client. Please contact its maintainers for support.

Release Notes

  • .NETCoreApp 2.1

    • No dependencies.
  • .NETFramework 4.5

  • .NETStandard 1.1

  • .NETStandard 2.0

  • MonoAndroid 1.0

    • No dependencies.
  • MonoTouch 1.0

    • No dependencies.
  • Portable Class Library (.NETFramework 4.5, Windows 8.0, WindowsPhoneApp 8.1)

  • UAP 10.0.16300

    • No dependencies.
  • Windows 8.0

  • WindowsPhoneApp 8.1

  • Xamarin.iOS 1.0

    • No dependencies.
  • Xamarin.Mac 2.0

    • No dependencies.
  • Xamarin.TVOS 1.0

    • No dependencies.
  • Xamarin.WatchOS 1.0

    • No dependencies.

NuGet packages (4)

Showing the top 4 NuGet packages that depend on System.Numerics.Tensors:

Package Downloads
Developing a C# wrapper to help developer easily create and train deep neural network models. Easy to use library, just focus on research Multiple backend - ArrayFire (In Progress), TensorSharp (In Progress), CNTK (Not Started), TensorFlow (Not Started), MxNet (Not Started) CUDA/ OpenCL support for some of the backends Light weight libray, built with .NET standard 2.0 Code well structured, easy to extend if you would like to extend with new layer, loss, metrics, optimizers, constraints, regularizer
Automatic tensor conversion for .NET
Dependency package for SiaNet and its backends.
This package contains ONNX Runtime for .Net platforms

GitHub repositories (3)

Showing the top 3 popular GitHub repositories that depend on System.Numerics.Tensors:

Repository Stars
A native memory manager for .NET
CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.

Version History