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Found 93 packages
Accord.Math
Contains a matrix extension library, along with a suite of numerical matrix decomposition methods, numerical optimization algorithms for constrained and unconstrained problems, special functions and other tools for scientific applications. This package is part of the Accord.NET Framework.
v3.8.0
↙ 8.6M / total
accord.netaforge.netframeworkmathdecompositions
CenterSpace.NMath.Standard.Windows.X64
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 solutions, random number generators, Fast Fourier Transforms (FFTs), numerical integration and differentiation methods, function minimization, curve fitting, root-finding, linear and nonlinear programming. This package also provides functions for statistical computation and biostatistics, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, multivariate statistics, partial least squares, and nonnegative matrix factorization. Built on .NET Standard 2.0. Requires a minimum of .NET Standard 2.0, .NET 5, .NET Core 2.0 or .NET Framework 4.6.1. Requires Visual Studio 2015-2019 C++ x64 runtime. Requires x64 hardware.
v7.4.2.16
↙ 954.0K / total
mathstatisticsnumericsprobabilitylinear
Extreme.Numerics
Numerics.NET (formerly Extreme Optimization Numerical Libraries for .NET) are a set of libraries for numerical computing and data analysis. This is the main package that contains all the core functionality. For optimal performance, we strongly recommend also referencing one of the native packages based on Intel's Math Kernel Library (MKL). Supports .NET 5.0-8.0+, .NET Framework 4.62+, .NET Standard 2.0, and .NET Core 3.1 on Windows, Linux and Mac.
v8.1.25
↙ 1.5M / total
mathstatisticsprobabilitymachinelearning
DiffSharp
DiffSharp is an automatic differentiation (AD) library. AD allows exact and efficient calculation of derivatives, by systematically invoking the chain rule of calculus at the elementary operator level during program execution. AD is different from numerical differentiation, which is prone to truncation and round-off errors, and symbolic differentiation, which is affected by expression swell and cannot fully handle algorithmic control flow. Using the DiffSharp library, derivative calculations (gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products) can be incorporated with minimal change into existing algorithms. Diffsharp supports nested forward and reverse AD up to any level, meaning that you can compute exact higher-order derivatives or differentiate functions that are internally making use of differentiation. Please see the API Overview page for a list of available operations. The library is under active development by Atılım Güneş Baydin and Barak A. Pearlmutter mainly for research applications in machine learning, as part of their work at the Brain and Computation Lab, Hamilton Institute, National University of Ireland Maynooth. DiffSharp is implemented in the F# language and can be used from C# and the other languages running on Mono or the .Net Framework, targeting the 64 bit platform. It is tested on Linux and Windows. We are working on interfaces/ports to other languages.
v0.7.7
↙ 56.2K / total
DifferentiationAutomaticSymbolicNumericalOptimization
pkg
DeepNet
Deep learning library for F#. Provides symbolic model differentiation, automatic differentiation and compilation to CUDA GPUs. Includes optimizers and model blocks used in deep learning. Make sure to set the platform of your project to x64.
v0.3.0.688
↙ 155.6K / total
MachineLearningDeepDeepNetSymbolic
pkg
DiffSharp.Core
DiffSharp is a tensor library with support for differentiable programming. It is designed for use in machine learning, probabilistic programming, optimization and other domains. For documentation and installation instructions visit: https://diffsharp.github.io/
v1.0.7
↙ 303.7K / total
F#fsharpMLAIMachine
CenterSpace.NMath.Standard.Linux.X64
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 solutions, random number generators, Fast Fourier Transforms (FFTs), numerical integration and differentiation methods, function minimization, curve fitting, root-finding, linear and nonlinear programming. This package also provides functions for statistical computation and biostatistics, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, multivariate statistics, partial least squares, and nonnegative matrix factorization. Built on .NET Standard 2.0. Requires a minimum of .NET Standard 2.0, .NET 5, .NET Core 2.0 or .NET Framework 4.6.1. Requires libgcc linux. Requires x64 hardware.
v7.4.2.16
↙ 333.6K / total
mathstatisticsnumericsprobabilitylinear
CenterSpace.NMath.Premium
The GPU-accelerated version of package CenterSpace.NMath. With a few minor exceptions, such as optional GPU configuration settings, the API is identical between CenterSpace.NMath.Premium and CenterSpace.NMath. If using at least .NET Framework 4.6.1 or .NET Core 2.0, we recommend using one of our NMath .NET Standard NuGet packages.
v6.2.0.58
↙ 343.5K / total
GPUmathstatisticsnumericslinear
CenterSpace.NMath
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 solutions, random number generators, Fast Fourier Transforms (FFTs), numerical integration and differentiation methods, function minimization, curve fitting, root-finding, linear and nonlinear programming. If you're using at least .NET Framework 4.6.1 or .NET Core 2.0, we recommend using one of our NMath .NET Standard NuGet packages.
v6.2.0.58
↙ 172.2K / total
mathstatisticsnumericslinearalgebra
xFunc.Maths
xFunc provides a powerful parser and analyzer for mathematical expressions. It excels at calculating derivatives, simplifying expressions, and achieving high performance.
v4.4.1
↙ 215.4K / total
mathformulaparseparsermath-parser
pkg
DiffSharp.Backends.Torch
DiffSharp is a tensor library with support for differentiable programming. It is designed for use in machine learning, probabilistic programming, optimization and other domains. For documentation and installation instructions visit: https://diffsharp.github.io/
v1.0.7
↙ 173.5K / total
F#fsharpMLAIMachine
pkg
DiffSharp.Backends.Reference
DiffSharp is a tensor library with support for differentiable programming. It is designed for use in machine learning, probabilistic programming, optimization and other domains. For documentation and installation instructions visit: https://diffsharp.github.io/
v1.0.7
↙ 151.7K / total
F#fsharpMLAIMachine
CenterSpace.NMath.Standard.Windows.X86.X64
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 solutions, random number generators, Fast Fourier Transforms (FFTs), numerical integration and differentiation methods, function minimization, curve fitting, root-finding, linear and nonlinear programming. This package also provides functions for statistical computation and biostatistics, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, multivariate statistics, partial least squares, and nonnegative matrix factorization. Built on .NET Standard 2.0. Requires a minimum of .NET Standard 2.0, .NET 5 or .NET Core 2.0. Does not support .NET Framework. Requires Visual Studio 2017 C++ x86 and x64 runtimes. Requires x64 hardware.
v7.1.0.47
↙ 178.4K / total
mathstatisticsnumericsprobabilitylinear
ParallelReverseAutoDiff
A library for parallelized reverse mode automatic differentiation in C# for custom neural network development.
v1.2.23
↙ 48.9K / total
autodiffautomatic-differentiationparallelreverse-modedifferentiation
pkg
DiffSharp-cuda-windows
DiffSharp is a tensor library with support for differentiable programming. It is designed for use in machine learning, probabilistic programming, optimization and other domains. For documentation and installation instructions visit: https://diffsharp.github.io/
v1.0.7
↙ 126.7K / total
F#fsharpMLAIMachine
CenterSpace.NMath.Standard.WindowsAndLinux.X64
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 solutions, random number generators, Fast Fourier Transforms (FFTs), numerical integration and differentiation methods, function minimization, curve fitting, root-finding, linear and nonlinear programming. This package also provides functions for statistical computation and biostatistics, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, multivariate statistics, partial least squares, and nonnegative matrix factorization. Built on .NET Standard 2.0. Requires a minimum of .NET Standard 2.0, .NET 5, .NET Core 2.0 or .NET Framework 4.6.1. Requires x64 hardware. On Windows, requires Visual Studio 2017 C++ x64 runtime. On Linux, requires libgcc.
v7.1.0.47
↙ 142.8K / total
mathstatisticsnumericsprobabilitylinear
pkg
DiffSharp-cuda-linux
DiffSharp is a tensor library with support for differentiable programming. It is designed for use in machine learning, probabilistic programming, optimization and other domains. For documentation and installation instructions visit: https://diffsharp.github.io/
v1.0.7
↙ 102.2K / total
F#fsharpMLAIMachine
pkg
DiffSharp.Data
DiffSharp is a tensor library with support for differentiable programming. It is designed for use in machine learning, probabilistic programming, optimization and other domains. For documentation and installation instructions visit: https://diffsharp.github.io/
v1.0.7
↙ 103.1K / total
F#fsharpMLAIMachine
pkg
DiffSharp-lite
DiffSharp is a tensor library with support for differentiable programming. It is designed for use in machine learning, probabilistic programming, optimization and other domains. For documentation and installation instructions visit: https://diffsharp.github.io/
v1.0.7
↙ 92.7K / total
F#fsharpMLAIMachine
pkg
DiffSharp-cpu
DiffSharp is a tensor library with support for differentiable programming. It is designed for use in machine learning, probabilistic programming, optimization and other domains. For documentation and installation instructions visit: https://diffsharp.github.io/
v1.0.7
↙ 95.6K / total
F#fsharpMLAIMachine
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