NumSharp 0.7.4

.NET Standard 2.0 This package targets .NET Standard 2.0. The package is compatible with this framework or higher.
There is a newer version of this package available.
See the version list below for details. 
dotnet add package NumSharp --version 0.7.4                

                    
                

            
NuGet\Install-Package NumSharp -Version 0.7.4
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="0.7.4" />
For projects that support PackageReference, copy this XML node into the project file to reference the package. 
paket add NumSharp --version 0.7.4                

                    
                

            
#r "nuget: NumSharp, 0.7.4"
#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=0.7.4

// Install NumSharp as a Cake Tool
#tool nuget:?package=NumSharp&version=0.7.4

NumSharp is the fundamental package for scientific computing with dot NET. It has implemented the arange, array, max, min, reshape, normalize, unique and random interfaces and so on. More and more interfaces will be added to the library gradually. If you want to use .NET to get started with machine learning, NumSharp will be your best tool library.

Product 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-browser 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)
Included target framework(s) (in package)
Learn more about Target Frameworks and .NET Standard.

NuGet packages (22)

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.
Microsoft.Quantum.Standard

Microsoft's Quantum standard libraries.
Bigtree.Algorithm

Machine Learning library in .NET Core.
FaceAiSharp

FaceAiSharp allows you to work with face-related computer vision tasks easily. It currently provides face detection, face recognition, facial landmarks detection, and eye state detection functionalities. FaceAiSharp leverages publicly available pretrained ONNX models to deliver accurate and efficient results and offers a convenient way to integrate them into your .NET applications. Whether you need to find faces, recognize individuals, detect facial landmarks, or determine eye states, FaceAiSharp simplifies the process with its simple API. ONNXRuntime is used for model inference, enabling hardware acceleration were possible. All processing is done locally, with no reliance on cloud services. This package contains just FaceAiSharp's managed code and does not include any ONNX models. Take a look at FaceAiSharp.Bundle for a batteries-included package with everything you need to get started.
FaceAiSharp.Bundle

FaceAiSharp allows you to work with face-related computer vision tasks easily. It currently provides face detection, face recognition, facial landmarks detection, and eye state detection functionalities. FaceAiSharp leverages publicly available pretrained ONNX models to deliver accurate and efficient results and offers a convenient way to integrate them into your .NET applications. Whether you need to find faces, recognize individuals, detect facial landmarks, or determine eye states, FaceAiSharp simplifies the process with its simple API. ONNXRuntime is used for model inference, enabling hardware acceleration were possible. All processing is done locally, with no reliance on cloud services. This is a bundle package that installs FaceAiSharp's managed code and multiple AI models in the ONNX format.

GitHub repositories (8)

Showing the top 5 popular GitHub repositories that depend on NumSharp:

Repository Stars
stakira/OpenUtau
Open singing synthesis platform / Open source UTAU successor
SciSharp/NumSharp
High Performance Computation for N-D Tensors in .NET, similar API to NumPy.
kendryte/nncase
Open deep learning compiler stack for Kendryte AI accelerators ✨
SciSharp/SiaNet
An easy to use C# deep learning library with CUDA/OpenCL support
microsoft/qsharp-runtime
Runtime components for Q#
Version Downloads Last updated
0.30.0 570,954 2/14/2021
0.20.5 722,497 12/31/2019
0.20.4 332,976 10/5/2019
0.20.3 3,554 9/28/2019
0.20.2 2,425 9/11/2019
0.20.1 18,094 9/1/2019
0.20.0 3,772 8/20/2019
0.10.6 26,856 7/24/2019
0.10.5 2,594 7/22/2019
0.10.4 2,464 7/18/2019
0.10.3 12,284 6/15/2019
0.10.2 2,138 5/25/2019
0.10.1 3,656 5/11/2019
0.10.0 2,403 5/5/2019
0.9.0 3,407 4/15/2019
0.8.3 1,995 3/29/2019
0.8.2 2,750 3/25/2019
0.8.1 2,171 3/22/2019
0.8.0 2,292 3/12/2019
0.7.4 1,860 3/7/2019
0.7.3 543,668 2/20/2019
0.7.2 2,346 2/18/2019
0.7.1 2,381 2/12/2019
0.7.0 1,931 1/28/2019
0.6.6 2,336 1/26/2019
0.6.5 2,370 1/11/2019
0.6.4 2,371 1/7/2019
0.6.3 1,878 12/30/2018
0.6.2 5,917 12/27/2018
0.6.1 1,882 12/26/2018
0.6.0 1,956 12/21/2018
0.5.0 1,922 12/5/2018
0.4.0 1,856 11/21/2018
0.3.0 1,851 11/7/2018
0.2.0 3,965 10/29/2018
0.1.0 2,113 10/10/2018

Added np.concatenate