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F# Data alternatives and similar packages

Based on the "Machine Learning and Data Science" category

  • ML.NET

    Cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers.
  • Accord.NET

    Machine learning framework combined with audio and image processing libraries (computer vision, computer audition, signal processing and statistics).
  • AForge.NET

    Framework for developers and researchers in the fields of Computer Vision and Artificial Intelligence (image processing, neural networks, genetic algorithms, machine learning, robotics).
  • Accord.NET Extensions

    Advanced image processing and computer vision algorithms made as fluent extensions.
  • Deedle

    Data frame and (time) series library for exploratory data manipulation with C# and F# support
  • encog-dotnet-core

    Encog Machine Learning Framework
  • numl

    Designed to include the most popular supervised and unsupervised learning algorithms while minimizing the friction involved with creating the predictive models.
  • Spreads

    Series and Panels for Real-time and Exploratory Analysis of Data Streams. Spreads library is optimized for performance and memory usage. It is several times faster than other open source projects.
  • R Provider

    Type provider that exposes R packages and functions in a type-safe way to F# callers
  • Catalyst

    Catalyst Cross-platform Natural Language Processing (NLP) library inspired by spaCy, with pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models. Part of the SciSharp Stack
  • Infer.NET

    A framework for running Bayesian inference in graphical models. It can also be used for probabilistic programming. [Proprietary] [Free] [Research]
  • FsLab

    A collection of data science and machine learning libraries for F# and .NET
  • Synapses

    An in-memory neural network library written in F#.
  • SciSharp STACK

    A rich machine learning ecosystem for .NET created by porting the most popular Python libraries to C#.

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