Top 20 NuGet algebra Packages

Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solut...

Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solut...

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

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

Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solut...

Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solut...

Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solut...

A simple cross platform .NET API for Intel MKL. Reference the MKL.NET package and required runtime packages and use the static MKL functions. The correct native libraries will be included and loaded at runtime. Exposing functions from MKL keeping the syntax as close to the c developer reference as...

The Extreme Optimization Numerical Libraries for .NET are a set of libraries for numerical computing and data analysis. This package contains the single-precision mixed-mode native provider. This is the recommended native provider for the classic .NET Framework on Windows. Supports .NET Framework ...

The Extreme Optimization Numerical Libraries for .NET are a set of libraries for numerical computing and data analysis. This package contains the single-precision mixed-mode native provider. This is the recommended native provider for the classic .NET Framework on Windows. Supports .NET Framework ...

The Extreme Optimization Numerical Libraries for .NET are a set of libraries for numerical computing and data analysis. This package contains the mixed-mode native provider. This is the recommended native provider for the classic .NET Framework on Windows. Supports .NET Framework 4.0 and 4.6+ on W...

The Extreme Optimization Numerical Libraries for .NET are a set of libraries for numerical computing and data analysis. This package contains the mixed-mode native provider. This is the recommended native provider for the classic .NET Framework on Windows. Supports .NET Framework 3.5 on Windows.

Powerful MatrixDotNet library for matrix calculation With all answers see documentation or ask on gitter or github issues

OpenBLAS native libraries for Math.NET Numerics.

Extreme Optimization Numerical Libraries for .NET P/Invoke MKL Provider for Windows.

Extreme Optimization Numerical Libraries for .NET Single-Precision P/Invoke MKL Provider for Windows.

Function programming goodness: algebraic structures, Maybe, Either, Unit, State, Writer, Functor, Monad, Monoid, Lenses, and more.

Intel MKL native libraries for Math.NET Numerics on Linux.

.NET library providing reusable math classes.

Bright Wire is an open source machine learning library. Includes neural networks (feed forward, convolutional and recurrent), naive bayes, linear regression, decision trees, logistic regression, k-means clustering and dimensionality reduction.