Programming language: - - -

FsLab alternatives and similar packages

Based on the "Machine Learning and Data Science" category.
Alternatively, view FsLab alternatives based on common mentions on social networks and blogs.

  • 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).
  • Build single-codebase applications for Windows, Web, Linux, macOS, iOS and Android with open-source Uno Platform. Fluent and Material design included in-the-box. Try now via 3 min tutorial.
    Sponsored platform.uno
  • TensorFlow.NET

    .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.
  • 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).
  • F# Data

    F# type providers for accessing XML, JSON, CSV and HTML files (based on sample documents) and for accessing WorldBank data
  • Deedle

    Data frame and (time) series library for exploratory data manipulation with C# and F# support
  • Accord.NET Extensions

    Advanced image processing and computer vision algorithms made as fluent extensions.
  • encog-dotnet-core

    Encog Machine Learning Framework
  • numl

    5.5 0.0 L3 FsLab VS 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
  • Synapses

    An in-memory neural network library written in F#.
  • Infer.NET

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

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

Do you think we are missing an alternative of FsLab or a related project?

Add another 'Machine Learning and Data Science' Package


FsLab project templates

This repository hosts FsLab project templates compatible with many F# editors. Using the templates, you can start with FsLab in 3 simple steps:

  1. Download a selected template
  2. Build it to download FsLab packages
  3. Open scripts in your editor & play!

1. Download a template

To get started, download a branch with the template as a ZIP file and extract the files. The repository currently hosts the following templates:

  • FsLab Basic template - references FsLab and gives you a single script file with simple demo
  • FsLab Journal template - in addition to the above, this template also lets you write literate FsLab scripts and produce HTML or LaTeX reports

2. Build the template

Visual Studio and Xamarin Studio should download FsLab packages automatically on build. If this doesn't happen (or when using other editors), you need to explicitly install them.

  • In the basic template, run paket install. For example, when using mono on Mac, run:

    mono .paket/paket.bootstrapper.exe
    mono .paket/paket.exe install
  • In the journal template you can use build scripts build.sh and build.cmd. The following will process all journals and open them in a web browser:

    chmod +x build.sh
    ./build.sh run

3. Use FsLab for fun & profit!

Once you have the template and packages, you can open the Tutorial.fsx file and start playing with FsLab. For more information, see the Getting Started page on www.fslab.org.