System.Numerics.Tensors
0.1.0
Prefix Reserved
See the version list below for details.
Requires NuGet 2.8.6 or higher.
dotnet add package System.Numerics.Tensors --version 0.1.0
NuGet\Install-Package System.Numerics.Tensors -Version 0.1.0
<PackageReference Include="System.Numerics.Tensors" Version="0.1.0" />
paket add System.Numerics.Tensors --version 0.1.0
#r "nuget: System.Numerics.Tensors, 0.1.0"
// Install System.Numerics.Tensors as a Cake Addin
#addin nuget:?package=System.Numerics.Tensors&version=0.1.0
// Install System.Numerics.Tensors as a Cake Tool
#tool nuget:?package=System.Numerics.Tensors&version=0.1.0
Tensor class which represents and extends multi-dimensional arrays.
Commonly Used Types:
System.Numerics.Tensors.Tensor<T>
System.Numerics.Tensors.CompressedSparseTensor<T>
System.Numerics.Tensors.DenseTensor<T>
System.Numerics.Tensors.SparseTensor<T>
| Product | Versions |
|---|---|
| .NET | net5.0 net5.0-windows net6.0 net6.0-android net6.0-ios net6.0-maccatalyst net6.0-macos net6.0-tvos net6.0-windows net7.0 net7.0-android net7.0-ios net7.0-maccatalyst net7.0-macos net7.0-tvos net7.0-windows |
| .NET Core | netcoreapp1.0 netcoreapp1.1 netcoreapp2.0 netcoreapp2.1 netcoreapp2.2 netcoreapp3.0 netcoreapp3.1 |
| .NET Standard | netstandard1.1 netstandard1.2 netstandard1.3 netstandard1.4 netstandard1.5 netstandard1.6 netstandard2.0 netstandard2.1 |
| .NET Framework | net45 net451 net452 net46 net461 net462 net463 net47 net471 net472 net48 net481 |
| MonoAndroid | monoandroid |
| MonoMac | monomac |
| MonoTouch | monotouch |
| Tizen | tizen30 tizen40 tizen60 |
| Universal Windows Platform | uap uap10.0 |
| Windows Phone | wpa81 |
| Windows Store | netcore netcore45 netcore451 |
| Xamarin.iOS | xamarinios |
| Xamarin.Mac | xamarinmac |
| Xamarin.TVOS | xamarintvos |
| Xamarin.WatchOS | xamarinwatchos |
-
.NETCoreApp 2.1
- No dependencies.
-
.NETFramework 4.5
- System.Memory (>= 4.5.0)
-
.NETStandard 1.1
- NETStandard.Library (>= 1.6.1)
- System.Memory (>= 4.5.0)
-
.NETStandard 2.0
- System.Memory (>= 4.5.0)
-
MonoAndroid 1.0
- No dependencies.
-
MonoTouch 1.0
- No dependencies.
-
Portable Class Library (.NETFramework 4.5, Windows 8.0, WindowsPhoneApp 8.1)
- System.Memory (>= 4.5.0)
-
UAP 10.0.16300
- No dependencies.
-
Windows 8.0
- System.Memory (>= 4.5.0)
-
WindowsPhoneApp 8.1
- System.Memory (>= 4.5.0)
-
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 (6)
Showing the top 5 NuGet packages that depend on System.Numerics.Tensors:
| Package | Downloads |
|---|---|
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SiaNet.Engine
Dependency package for SiaNet and its backends. |
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SiaNet
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 |
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AutoTensor
Automatic tensor conversion for .NET |
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CensorCore
The core package for CensorCore, a flexible and modular framework for censoring NSFW images based on the NudeNet ML model. |
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Aiinfra.OnnxRuntime.Gpu
This package contains ONNX Runtime for .Net platforms |
GitHub repositories (5)
Showing the top 5 popular GitHub repositories that depend on System.Numerics.Tensors:
| Repository | Stars |
|---|---|
|
dotnet/TorchSharp
A .NET library that provides access to the library that powers PyTorch.
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allisterb/jemalloc.NET
A native memory manager for .NET
|
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microsoft/CryptoNets
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.
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Amine-Smahi/C-Sharp-Learning-Journey
Some of the projects i made when starting to learn c#, winfroms and wpf
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dotnet-architecture/MNISTTensorCNTK
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