NumSharp 0.30.0
.NET Standard 2.0
dotnet add package NumSharp --version 0.30.0
NuGet\Install-Package NumSharp -Version 0.30.0
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package.
<PackageReference Include="NumSharp" Version="0.30.0" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add NumSharp --version 0.30.0
The NuGet Team does not provide support for this client. Please contact its maintainers for support.
#r "nuget: NumSharp, 0.30.0"
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package.
// Install NumSharp as a Cake Addin
#addin nuget:?package=NumSharp&version=0.30.0
// Install NumSharp as a Cake Tool
#tool nuget:?package=NumSharp&version=0.30.0
The NuGet Team does not provide support for this client. Please contact its maintainers for support.
NumSharp is the fundamental library for scientific computing with .NET providing a similar API to python's numpy scientific library. NumSharp has full N-D, broadcasting and axis support. If you want to use .NET to get started with machine learning, NumSharp will be your best tool.
| Product | Versions Compatible and additional computed target framework versions. |
|---|---|
| .NET | net5.0 was computed. net5.0-windows was computed. net6.0 was computed. net6.0-android was computed. net6.0-ios was computed. net6.0-maccatalyst was computed. net6.0-macos was computed. net6.0-tvos was computed. net6.0-windows was computed. net7.0 was computed. net7.0-android was computed. net7.0-ios was computed. net7.0-maccatalyst was computed. net7.0-macos was computed. net7.0-tvos was computed. net7.0-windows was computed. net8.0 was computed. net8.0-android was computed. net8.0-ios was computed. net8.0-maccatalyst was computed. net8.0-macos was computed. net8.0-tvos was computed. net8.0-windows was computed. |
| .NET Core | netcoreapp2.0 was computed. netcoreapp2.1 was computed. netcoreapp2.2 was computed. netcoreapp3.0 was computed. netcoreapp3.1 was computed. |
| .NET Standard | netstandard2.0 is compatible. netstandard2.1 was computed. |
| .NET Framework | net461 was computed. net462 was computed. net463 was computed. net47 was computed. net471 was computed. net472 was computed. net48 was computed. net481 was computed. |
| MonoAndroid | monoandroid was computed. |
| MonoMac | monomac was computed. |
| MonoTouch | monotouch was computed. |
| Tizen | tizen40 was computed. tizen60 was computed. |
| Xamarin.iOS | xamarinios was computed. |
| Xamarin.Mac | xamarinmac was computed. |
| Xamarin.TVOS | xamarintvos was computed. |
| Xamarin.WatchOS | xamarinwatchos was computed. |
Compatible target framework(s)
Additional computed target framework(s)
Learn more about Target Frameworks and .NET Standard.
-
.NETStandard 2.0
- System.Memory (>= 4.5.4)
NuGet packages (17)
Showing the top 5 NuGet packages that depend on NumSharp:
| Package | Downloads |
|---|---|
Microsoft.Quantum.Simulators
Classical simulators of quantum computers for the Q# programming language. |
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Microsoft.Quantum.Standard
Microsoft's Quantum standard libraries. |
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Bigtree.Algorithm
Machine Learning library in .NET Core. |
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Microsoft.Quantum.Standard.Visualization
Provides IQ# visualization support for Microsoft's Q# standard libraries. |
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our1314.work
Package Description |
GitHub repositories (7)
Showing the top 5 popular GitHub repositories that depend on NumSharp:
| Repository | Stars |
|---|---|
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stakira/OpenUtau
Open singing synthesis platform / Open source UTAU successor
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SciSharp/NumSharp
High Performance Computation for N-D Tensors in .NET, similar API to NumPy.
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SciSharp/SiaNet
An easy to use C# deep learning library with CUDA/OpenCL support
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microsoft/qsharp-runtime
Runtime components for Q#
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microsoft/Microsoft-Rocket-Video-Analytics-Platform
A highly extensible software stack to empower everyone to build practical real-world live video analytics applications for object detection and counting with cutting edge machine learning algorithms.
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| Version | Downloads | Last updated |
|---|---|---|
| 0.30.0 | 206,126 | 2/14/2021 |
| 0.20.5 | 639,202 | 12/31/2019 |
| 0.20.4 | 307,259 | 10/5/2019 |
| 0.20.3 | 2,458 | 9/28/2019 |
| 0.20.2 | 1,692 | 9/11/2019 |
| 0.20.1 | 16,257 | 9/1/2019 |
| 0.20.0 | 2,645 | 8/20/2019 |
| 0.10.6 | 23,843 | 7/24/2019 |
| 0.10.5 | 1,510 | 7/22/2019 |
| 0.10.4 | 1,450 | 7/18/2019 |
| 0.10.3 | 9,615 | 6/15/2019 |
| 0.10.2 | 1,467 | 5/25/2019 |
| 0.10.1 | 2,737 | 5/11/2019 |
| 0.10.0 | 1,557 | 5/5/2019 |
| 0.9.0 | 2,360 | 4/15/2019 |
| 0.8.3 | 1,278 | 3/29/2019 |
| 0.8.2 | 1,750 | 3/25/2019 |
| 0.8.1 | 1,354 | 3/22/2019 |
| 0.8.0 | 1,561 | 3/12/2019 |
| 0.7.4 | 1,147 | 3/7/2019 |
| 0.7.3 | 454,794 | 2/20/2019 |
| 0.7.2 | 1,430 | 2/18/2019 |
| 0.7.1 | 1,445 | 2/12/2019 |
| 0.7.0 | 1,212 | 1/28/2019 |
| 0.6.6 | 1,414 | 1/26/2019 |
| 0.6.5 | 1,505 | 1/11/2019 |
| 0.6.4 | 1,526 | 1/7/2019 |
| 0.6.3 | 1,180 | 12/30/2018 |
| 0.6.2 | 3,432 | 12/27/2018 |
| 0.6.1 | 1,154 | 12/26/2018 |
| 0.6.0 | 1,220 | 12/21/2018 |
| 0.5.0 | 1,262 | 12/5/2018 |
| 0.4.0 | 1,148 | 11/21/2018 |
| 0.3.0 | 1,173 | 11/7/2018 |
| 0.2.0 | 2,504 | 10/29/2018 |
| 0.1.0 | 1,388 | 10/10/2018 |