Numpy.Bare 3.8.1.25

C# bindings for NumPy on Win64 - a fundamental library for scientific computing, machine learning and AI. Does require Python 3.8 with NumPy 1.16 installed!

Install-Package Numpy.Bare -Version 3.8.1.25
dotnet add package Numpy.Bare --version 3.8.1.25
<PackageReference Include="Numpy.Bare" Version="3.8.1.25" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add Numpy.Bare --version 3.8.1.25
The NuGet Team does not provide support for this client. Please contact its maintainers for support.
#r "nuget: Numpy.Bare, 3.8.1.25"
For F# scripts that support #r syntax, copy this into the source code to reference the package.

NuGet packages (2)

Showing the top 2 NuGet packages that depend on Numpy.Bare:

Package Downloads
Keras.NET
C# bindings for Keras on Win64 - Keras.NET is a high-level neural networks API, capable of running on top of TensorFlow, CNTK, or Theano.
Torch.NET
Torch.NET brings the awesome Python package PyTorch to the .NET world. PyTorch offers Tensor computations and more with efficient GPU or multi-core CPU processing support and is to be considered one of the fundamental libraries for scientific computing, machine learning and AI in Python. Torch.NET empowers .NET developers to leverage PyTorch's extensive functionality including computational graphs with with multi-dimensional arrays, back-propagation, neural network implementations and many more via a compatible strong-typed API.

GitHub repositories (3)

Showing the top 3 popular GitHub repositories that depend on Numpy.Bare:

Repository Stars
SciSharp/Keras.NET
Keras.NET is a high-level neural networks API for C# and F#, with Python Binding and capable of running on top of TensorFlow, CNTK, or Theano.
SciSharp/Numpy.NET
C#/F# bindings for NumPy - a fundamental library for scientific computing, machine learning and AI
SciSharp/Torch.NET
.NET bindings for PyTorch. Machine Learning with C# / F# with Multi-GPU/CPU support

Version History

Version Downloads Last updated
3.8.1.25 1,363 11/12/2020
3.8.1.24 170 11/7/2020
3.8.1.23 243 10/1/2020
3.8.1.22 1,490 8/3/2020
3.7.1.25 198 11/12/2020
3.7.1.24 149 11/7/2020
3.7.1.23 102 10/1/2020
3.7.1.22 470 8/3/2020
3.7.1.21 179 7/22/2020
3.7.1.20 162 7/15/2020
3.7.1.19 147 7/13/2020
3.7.1.18 132 7/8/2020
3.7.1.17 136 7/7/2020
3.7.1.16 296 6/18/2020
3.7.1.15 151 6/7/2020
3.7.1.14 379 4/21/2020
3.7.1.13 149 4/20/2020
3.7.1.12 274 3/30/2020
3.7.1.11 3,043 2/25/2020
3.7.1.10 2,442 10/11/2019
3.7.1.9 495 8/2/2019
3.7.1.6 816 7/8/2019
3.7.1.5 222 7/4/2019
3.7.1.4 367 6/30/2019
3.7.1.3 211 6/25/2019
3.7.1.2 212 6/23/2019
3.7.1.1 252 6/19/2019
3.6.1.25 77 11/12/2020
3.6.1.24 138 11/7/2020
3.6.1.23 103 10/1/2020
3.6.1.22 185 8/3/2020
3.6.1.21 115 7/22/2020
3.6.1.20 138 7/15/2020
3.6.1.19 136 7/13/2020
3.6.1.18 106 7/8/2020
3.6.1.17 118 7/7/2020
3.6.1.16 197 6/18/2020
3.6.1.15 113 6/7/2020
3.6.1.14 153 4/21/2020
3.6.1.13 128 4/20/2020
3.6.1.12 179 3/30/2020
3.6.1.11 376 2/25/2020
3.6.1.10 829 10/11/2019
3.6.1.9 4,179 8/2/2019
3.6.1.6 397 7/8/2019
3.6.1.5 186 7/4/2019
3.6.1.4 294 6/30/2019
3.6.1.3 241 6/25/2019
3.6.1.1 716 6/19/2019
3.5.1.25 60 11/12/2020
3.5.1.24 135 11/7/2020
3.5.1.23 78 10/1/2020
3.5.1.22 158 8/3/2020
3.5.1.21 110 7/22/2020
3.5.1.20 124 7/15/2020
3.5.1.19 130 7/13/2020
3.5.1.18 102 7/8/2020
3.5.1.17 117 7/7/2020
3.5.1.16 179 6/18/2020
3.5.1.15 208 6/7/2020
3.5.1.14 118 4/21/2020
3.5.1.13 128 4/20/2020
3.5.1.12 162 3/30/2020
3.5.1.11 205 2/25/2020
3.5.1.10 276 10/11/2019
3.5.1.9 340 8/2/2019
3.5.1.6 225 7/8/2019
3.5.1.5 197 7/4/2019
3.5.1.1 264 6/19/2019
2.7.1.25 70 11/12/2020
2.7.1.24 137 11/7/2020
2.7.1.23 78 10/1/2020
2.7.1.22 166 8/3/2020
2.7.1.21 108 7/22/2020
2.7.1.20 123 7/15/2020
2.7.1.19 132 7/13/2020
2.7.1.18 115 7/8/2020
2.7.1.17 117 7/7/2020
2.7.1.16 185 6/18/2020
2.7.1.15 99 6/7/2020
2.7.1.14 124 4/21/2020
2.7.1.13 124 4/20/2020
2.7.1.12 159 3/30/2020
2.7.1.11 209 2/25/2020
2.7.1.10 269 10/11/2019
2.7.1.9 330 8/2/2019
2.7.1.6 220 7/8/2019
2.7.1.5 208 7/4/2019
1.1.0 318 6/16/2019
1.0.0 244 6/8/2019