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

9.6 7.2 FsLab VS ML.NET

ML.NET is an open source and cross-platform machine learning framework for .NET.
Accord.NET

9.5 6.7 L2 FsLab VS Accord.NET

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

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TensorFlow.NET

8.3 2.1 FsLab VS TensorFlow.NET

.NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.
m2cgen

7.7 5.4 FsLab VS m2cgen

Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
AForge.NET

7.1 0.0 L2 FsLab VS AForge.NET

AForge.NET Framework is a C# framework designed for developers and researchers in the fields of Computer Vision and Artificial Intelligence - image processing, neural networks, genetic algorithms, machine learning, robotics, etc.
F# Data

6.3 6.1 FsLab VS F# Data

F# Data: Library for Data Access
Deedle

6.3 3.7 FsLab VS Deedle

Easy to use .NET library for data and time series manipulation and for scientific programming
Accord.NET Extensions

6.1 0.0 L5 FsLab VS Accord.NET Extensions

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

5.5 0.0 FsLab VS encog-dotnet-core

Encog Machine Learning Framework
numl

5.3 0.0 L3 FsLab VS numl

Machine Learning for .NET
Catalyst

5.0 5.6 FsLab VS Catalyst

🚀 Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.
Spreads

4.6 3.7 FsLab VS Spreads

Series and Panels for Real-time and Exploratory Analysis of Data Streams
R Provider

4.3 0.0 FsLab VS R Provider

Access R packages from F#
Synapses

2.3 0.0 FsLab VS Synapses

A group of neural-network libraries for functional and mainstream languages
Infer.NET

1.8 0.0 FsLab VS Infer.NET

UAI 2015. Kernel-based just-in-time learning for expectation propagation
SciSharp STACK

- FsLab VS SciSharp STACK

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

* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.

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README

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:

Download a selected template
Build it to download FsLab packages
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.