NumpyDotNet 0.9.79
Install-Package NumpyDotNet -Version 0.9.79
dotnet add package NumpyDotNet --version 0.9.79
<PackageReference Include="NumpyDotNet" Version="0.9.79" />
paket add NumpyDotNet --version 0.9.79
#r "nuget: NumpyDotNet, 0.9.79"
// Install NumpyDotNet as a Cake Addin
#addin nuget:?package=NumpyDotNet&version=0.9.79
// Install NumpyDotNet as a Cake Tool
#tool nuget:?package=NumpyDotNet&version=0.9.79
This library provides a 100% pure .NET implementation of the NumPy API. Multi-threaded, fast and accurate.
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 |
.NET Core | netcoreapp2.0 netcoreapp2.1 netcoreapp2.2 netcoreapp3.0 netcoreapp3.1 |
.NET Standard | netstandard2.0 netstandard2.1 |
.NET Framework | net461 net462 net463 net47 net471 net472 net48 |
MonoAndroid | monoandroid |
MonoMac | monomac |
MonoTouch | monotouch |
Tizen | tizen40 tizen60 |
Xamarin.iOS | xamarinios |
Xamarin.Mac | xamarinmac |
Xamarin.TVOS | xamarintvos |
Xamarin.WatchOS | xamarinwatchos |
-
- Microsoft.CSharp (>= 4.5.0)
- NETStandard.Library (>= 2.0.3)
- System.Dynamic.Runtime (>= 4.3.0)
NuGet packages (3)
Showing the top 3 NuGet packages that depend on NumpyDotNet:
Package | Downloads |
---|---|
MxNet.Sharp
C# Binding for the Apache MxNet library. NDArray, Symbolic and Gluon Supported MxNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. MXNet is more than a deep learning project. It is a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers. |
|
lafd4net
A port of light-anime-face-detector to .NET 5.0, which is based on LFFD, a Light and Fast Face Detector for Edge Devices |
|
Onnx.Net
Open Neural Network Exchange (ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Currently we focus on the capabilities needed for inferencing (scoring). |
GitHub repositories (1)
Showing the top 1 popular GitHub repositories that depend on NumpyDotNet:
Repository | Stars |
---|---|
deepakkumar1984/MxNet.Sharp
.NET Standard bindings for Apache MxNet with Imperative, Symbolic and Gluon Interface for developing, training and deploying Machine Learning models in C#. https://mxnet.tech-quantum.com/
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Version | Downloads | Last updated | |
---|---|---|---|
0.9.79 | 339 | 5/29/2022 | |
0.9.78 | 141 | 5/15/2022 | |
0.9.77 | 303 | 4/10/2022 | |
0.9.76 | 112 | 3/25/2022 | |
0.9.75 | 918 | 10/19/2021 | |
0.9.74 | 1,664 | 4/25/2021 | |
0.9.73 | 197 | 4/18/2021 | |
0.9.72 | 143 | 4/16/2021 | |
0.9.71 | 172 | 4/15/2021 | |
0.9.70 | 611 | 3/10/2021 | |
0.9.63 | 511 | 2/13/2021 | |
0.9.62 | 525 | 1/24/2021 | |
0.9.61 | 258 | 12/30/2020 | |
0.9.60 | 235 | 12/22/2020 | |
0.9.55 | 311 | 11/27/2020 | |
0.9.54 | 233 | 11/22/2020 | |
0.9.53 | 271 | 11/13/2020 | |
0.9.52 | 450 | 9/30/2020 | |
0.9.50 | 413 | 8/10/2020 | |
0.9.42 | 2,247 | 3/12/2020 | |
0.9.40 | 414 | 3/4/2020 | |
0.9.35.3 | 395 | 3/3/2020 | |
0.9.35.2 | 378 | 3/2/2020 | |
0.9.35.1 | 380 | 3/2/2020 | |
0.9.35 | 407 | 3/1/2020 | |
0.9.30 | 484 | 2/23/2020 | |
0.9.21 | 364 | 2/12/2020 | |
0.9.14.3 | 301 | 2/8/2020 | |
0.9.14.2 | 266 | 2/7/2020 | |
0.9.14.1 | 312 | 2/5/2020 | |
0.9.14 | 531 | 1/30/2020 | |
0.9.12 | 420 | 1/13/2020 | |
0.9.10 | 408 | 1/7/2020 | |
0.9.8 | 365 | 12/27/2019 | |
0.9.5 | 372 | 12/18/2019 |
fix for np.delete with axis. improved support for custom data types.