NumpyDotNet 0.9.86.2

dotnet add package NumpyDotNet --version 0.9.86.2
NuGet\Install-Package NumpyDotNet -Version 0.9.86.2
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="NumpyDotNet" Version="0.9.86.2" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add NumpyDotNet --version 0.9.86.2
#r "nuget: NumpyDotNet, 0.9.86.2"
#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 NumpyDotNet as a Cake Addin
#addin nuget:?package=NumpyDotNet&version=0.9.86.2

// Install NumpyDotNet as a Cake Tool
#tool nuget:?package=NumpyDotNet&version=0.9.86.2

This library provides a 100% pure .NET implementation of the NumPy API.  Multi-threaded, fast and accurate.

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 (5)

Showing the top 5 NuGet packages that depend on NumpyDotNet:

Package Downloads
RL.Env

RL.Env is an open source dotnet library for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well a standard set of environments compliant with that API.

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.

PdfOcr

Pdf OCR library based on paddle OCR

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 (2)

Showing the top 2 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/
Quansight-Labs/numpy.net
A port of NumPy to .Net
Version Downloads Last updated
0.9.86.2 1,307 1/20/2024
0.9.86.1 1,601 10/11/2023
0.9.86 268 9/29/2023
0.9.85.1 1,426 5/15/2023
0.9.85 170 5/11/2023
0.9.84 720 4/9/2023
0.9.83.9 256 4/2/2023
0.9.83.8 223 4/2/2023
0.9.83.7 202 3/31/2023
0.9.83.6 37,208 12/28/2022
0.9.83.5 318 12/27/2022
0.9.83.4 301 12/27/2022
0.9.83.3 368 12/10/2022
0.9.83.2 308 12/9/2022
0.9.83.1 309 12/8/2022
0.9.83 331 12/7/2022
0.9.82.1 3,087 10/28/2022
0.9.82 412 10/25/2022
0.9.81 388 10/23/2022
0.9.80.5 496 10/21/2022
0.9.80.4 1,215 9/26/2022
0.9.80.3 641 9/10/2022
0.9.80.2 424 9/9/2022
0.9.80.1 405 9/9/2022
0.9.80 461 9/3/2022
0.9.79 4,226 5/29/2022
0.9.78 533 5/15/2022
0.9.77 722 4/10/2022
0.9.76 503 3/25/2022
0.9.75 1,882 10/19/2021
0.9.74 2,921 4/25/2021
0.9.73 407 4/18/2021
0.9.72 350 4/16/2021
0.9.71 381 4/15/2021
0.9.70 1,126 3/10/2021
0.9.63 719 2/13/2021
0.9.62 899 1/24/2021
0.9.61 488 12/30/2020
0.9.60 445 12/22/2020
0.9.55 531 11/27/2020
0.9.54 444 11/22/2020
0.9.53 489 11/13/2020
0.9.52 669 9/30/2020
0.9.50 596 8/10/2020
0.9.42 4,234 3/12/2020
0.9.40 612 3/4/2020
0.9.35.3 624 3/3/2020
0.9.35.2 614 3/2/2020
0.9.35.1 608 3/2/2020
0.9.35 646 3/1/2020
0.9.30 726 2/23/2020
0.9.21 594 2/12/2020
0.9.14.3 507 2/8/2020
0.9.14.2 468 2/7/2020
0.9.14.1 533 2/5/2020
0.9.14 790 1/30/2020
0.9.12 648 1/13/2020
0.9.10 648 1/7/2020
0.9.8 565 12/27/2019
0.9.5 589 12/18/2019

fix customer reported bug in np.convolve