NumSharp 0.20.4
NumSharp is the fundamental library for scientific computing with .NET providing a similar API to python's numpy scientific library. NumSharp has full N-D, broadcasting and axis support. If you want to use .NET to get started with machine learning, NumSharp will be your best tool.
Showing the top 20 packages that depend on NumSharp.
| Packages | Downloads |
|---|---|
|
TensorFlow.NET
Google's TensorFlow full binding in .NET Standard.
Building, training and infering deep learning models.
https://tensorflownet.readthedocs.io
|
11 |
|
TensorFlow.NET
Google's TensorFlow full binding in .NET Standard.
Building, training and infering deep learning models.
https://tensorflownet.readthedocs.io
|
10 |
|
TensorFlow.NET
Google's TensorFlow full binding in .NET Standard.
Docs: https://tensorflownet.readthedocs.io
|
10 |
|
TensorFlow.NET
Google's TensorFlow full binding in .NET Standard.
Docs: https://tensorflownet.readthedocs.io
|
9 |
|
TensorFlow.NET
Google's TensorFlow full binding in .NET Standard.
Building, training and infering deep learning models.
https://tensorflownet.readthedocs.io
|
9 |
|
TensorFlow.NET
TensorFlow binding for .NET Standard.
|
7 |
|
TensorFlow.NET
Google's TensorFlow full binding in .NET Standard.
Docs: https://tensorflownet.readthedocs.io
|
7 |
|
TensorFlow.NET
Google's TensorFlow binding in .NET Standard.
Docs: https://tensorflownet.readthedocs.io
|
5 |
Support for np.newaxis and ellipsis (...) slicing. Added: np.transpose, np.swapaxes, ndarray.T, np.moveaxis, np.rollaxis, np.size, np.copyto, np.ceil, np.arccos, np.floor, np.modf, np.square, np.round, np.sign, np.arcsin, np.arctan, np.random.beta, np.random.gamma, np.random.bernoulli, np.random.binomial, np.random.lognormal, np.random.normal, np.random.poisson, np.random.chisquare, np.random.geometric.
Performance optimization for np.array, np.linspace, Randomizer class and all np.random.* methods.
.NET Standard 2.0
- System.Memory (>= 4.5.3)
- System.Runtime.CompilerServices.Unsafe (>= 4.5.2)
| Version | Downloads | Last updated |
|---|---|---|
| 0.30.0 | 7 | 04/18/2025 |
| 0.20.5 | 7 | 04/19/2025 |
| 0.20.4 | 7 | 04/19/2025 |
| 0.20.3 | 7 | 04/18/2025 |
| 0.20.2 | 7 | 04/18/2025 |
| 0.20.1 | 7 | 04/18/2025 |
| 0.20.0 | 7 | 04/18/2025 |
| 0.10.6 | 7 | 04/19/2025 |
| 0.10.5 | 7 | 04/19/2025 |
| 0.10.4 | 6 | 04/19/2025 |
| 0.10.3 | 7 | 04/18/2025 |
| 0.10.2 | 7 | 04/18/2025 |
| 0.10.1 | 7 | 04/18/2025 |
| 0.10.0 | 7 | 04/18/2025 |
| 0.9.0 | 7 | 04/18/2025 |
| 0.8.3 | 7 | 04/18/2025 |
| 0.8.2 | 7 | 04/18/2025 |
| 0.8.1 | 7 | 04/18/2025 |
| 0.8.0 | 7 | 04/18/2025 |
| 0.7.4 | 6 | 04/18/2025 |
| 0.7.3 | 7 | 04/18/2025 |
| 0.7.2 | 7 | 04/18/2025 |
| 0.7.1 | 7 | 04/18/2025 |
| 0.7.0 | 7 | 04/18/2025 |
| 0.6.6 | 7 | 04/19/2025 |
| 0.6.5 | 7 | 04/19/2025 |
| 0.6.4 | 6 | 04/19/2025 |
| 0.6.3 | 7 | 04/18/2025 |
| 0.6.2 | 6 | 04/18/2025 |
| 0.6.1 | 7 | 04/18/2025 |
| 0.6.0 | 8 | 04/18/2025 |
| 0.5.0 | 7 | 04/18/2025 |
| 0.4.0 | 7 | 04/18/2025 |
| 0.3.0 | 7 | 04/18/2025 |
| 0.2.0 | 7 | 04/18/2025 |
| 0.1.0 | 7 | 04/18/2025 |