NumpyDotNet 0.9.83.1
See the version list below for details.
dotnet add package NumpyDotNet --version 0.9.83.1
NuGet\Install-Package NumpyDotNet -Version 0.9.83.1
<PackageReference Include="NumpyDotNet" Version="0.9.83.1" />
paket add NumpyDotNet --version 0.9.83.1
#r "nuget: NumpyDotNet, 0.9.83.1"
// Install NumpyDotNet as a Cake Addin #addin nuget:?package=NumpyDotNet&version=0.9.83.1 // Install NumpyDotNet as a Cake Tool #tool nuget:?package=NumpyDotNet&version=0.9.83.1
Adding ndarray.ToSystemArray()
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-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. |
-
- Microsoft.CSharp (>= 4.5.0)
- NETStandard.Library (>= 2.0.3)
- System.Dynamic.Runtime (>= 4.3.0)
NuGet packages (5)
Showing the top 5 NuGet packages that depend on NumpyDotNet:
Package | Downloads |
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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. |
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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. |
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PdfOcr
Pdf OCR library based on paddle OCR |
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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 |
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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 |
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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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Quansight-Labs/numpy.net
A port of NumPy to .Net
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Version | Downloads | Last updated | |
---|---|---|---|
0.9.86.2 | 8,838 | 1/20/2024 | |
0.9.86.1 | 2,046 | 10/11/2023 | |
0.9.86 | 325 | 9/29/2023 | |
0.9.85.1 | 2,674 | 5/15/2023 | |
0.9.85 | 185 | 5/11/2023 | |
0.9.84 | 779 | 4/9/2023 | |
0.9.83.9 | 291 | 4/2/2023 | |
0.9.83.8 | 248 | 4/2/2023 | |
0.9.83.7 | 228 | 3/31/2023 | |
0.9.83.6 | 53,387 | 12/28/2022 | |
0.9.83.5 | 352 | 12/27/2022 | |
0.9.83.4 | 325 | 12/27/2022 | |
0.9.83.3 | 397 | 12/10/2022 | |
0.9.83.2 | 339 | 12/9/2022 | |
0.9.83.1 | 333 | 12/8/2022 | |
0.9.83 | 358 | 12/7/2022 | |
0.9.82.1 | 3,143 | 10/28/2022 | |
0.9.82 | 436 | 10/25/2022 | |
0.9.81 | 418 | 10/23/2022 | |
0.9.80.5 | 520 | 10/21/2022 | |
0.9.80.4 | 1,241 | 9/26/2022 | |
0.9.80.3 | 669 | 9/10/2022 | |
0.9.80.2 | 451 | 9/9/2022 | |
0.9.80.1 | 428 | 9/9/2022 | |
0.9.80 | 488 | 9/3/2022 | |
0.9.79 | 5,206 | 5/29/2022 | |
0.9.78 | 559 | 5/15/2022 | |
0.9.77 | 752 | 4/10/2022 | |
0.9.76 | 526 | 3/25/2022 | |
0.9.75 | 1,967 | 10/19/2021 | |
0.9.74 | 2,957 | 4/25/2021 | |
0.9.73 | 439 | 4/18/2021 | |
0.9.72 | 380 | 4/16/2021 | |
0.9.71 | 408 | 4/15/2021 | |
0.9.70 | 1,232 | 3/10/2021 | |
0.9.63 | 752 | 2/13/2021 | |
0.9.62 | 975 | 1/24/2021 | |
0.9.61 | 514 | 12/30/2020 | |
0.9.60 | 470 | 12/22/2020 | |
0.9.55 | 559 | 11/27/2020 | |
0.9.54 | 478 | 11/22/2020 | |
0.9.53 | 513 | 11/13/2020 | |
0.9.52 | 705 | 9/30/2020 | |
0.9.50 | 622 | 8/10/2020 | |
0.9.42 | 4,361 | 3/12/2020 | |
0.9.40 | 643 | 3/4/2020 | |
0.9.35.3 | 657 | 3/3/2020 | |
0.9.35.2 | 642 | 3/2/2020 | |
0.9.35.1 | 636 | 3/2/2020 | |
0.9.35 | 674 | 3/1/2020 | |
0.9.30 | 760 | 2/23/2020 | |
0.9.21 | 616 | 2/12/2020 | |
0.9.14.3 | 530 | 2/8/2020 | |
0.9.14.2 | 491 | 2/7/2020 | |
0.9.14.1 | 564 | 2/5/2020 | |
0.9.14 | 816 | 1/30/2020 | |
0.9.12 | 682 | 1/13/2020 | |
0.9.10 | 673 | 1/7/2020 | |
0.9.8 | 591 | 12/27/2019 | |
0.9.5 | 614 | 12/18/2019 |
Adding ndarray.ToSystemArray() to support cases where we don't know the type and dimensions of the array.