71 packages returned for Tags:"differentiation"

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... More information

Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general... More information

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... More information

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... More information

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... More information

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.... More information

Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general... More information

xFunc provides a powerful parser and analyzer for mathematical expressions. It excels at calculating derivatives, simplifying expressions, and achieving high performance.

Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general... More information

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... More information

Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general... More information

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... More information

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... More information

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... More information

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... More information

Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general... More information

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... More information

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... More information

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... More information

Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general... More information