Sharpest F#

A framework for scientific computing in .NET, encompassing numerical linear algebra, optimization, statistics, and more.

A library for fitting Gaussian process regression models in F#, facilitating advanced statistical modeling.

An open library for data and time series manipulation, supporting structured data frames and time series.

A deep learning library for F# offering symbolic model differentiation, automatic differentiation, and compilation to CUDA GPUs.

An automatic differentiation library for F#, providing exact and efficient gradients for machine learning and optimization.

A lightweight linear algebra library supporting generic types, enabling writing generic linear algebra code with custom numeric types.

A library for statistical computing in F#, offering tools for data analytics and statistical testing.

A collection of F# packages for data science, including data analysis, visualization, and machine learning tools.

F# for Jupyter Notebooks, enabling interactive data analysis and visualization.

A CLI tool to transpile trained classic ML models into native F# code with zero dependencies.

An open-source numerical library for .NET and Mono, providing methods and algorithms for numerical computations.

A basic open-source computer algebra library for .NET, Silverlight, and Mono, written entirely in F#.

A .NET open-source framework and runtime for large-scale computations in cloud computing environments.

An open-source, cross-platform machine learning framework for .NET developers to create custom ML models.

A machine learning library intended to ease the use of standard modeling techniques for both prediction and clustering.

SIMD-enhanced array extensions for faster computation in F#.

A neural network library in F#, facilitating the creation and training of neural networks.

A machine learning application using a deep belief network and connecting to NVIDIA GPUs via Alea.cuBase.