awesome-dotnet-datascience

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

A .NET library that provides fast, accurate and automatic differentiation (computes derivative / gradient) of mathematical functions.

DiffSharp allows for exact and efficient calculation of derivatives, by systematically invoking the chain rule of calculus at the elementary operator level during program execution.

FsLab is a curated collection of open source F# packages for data-science. Together with your editor or Jupyter notebook these packages allow you to rapidly develop scalable, high-performance analytics and visualizations using succinct, type-safe, production-ready code.

A framework for running Bayesian inference in graphical models. It can also be used for probabilistic programming.

Keras.NET is a high-level neural networks API, written in C# with Python Binding and capable of running on top of TensorFlow, CNTK, or Theano.

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

Cross platform wrapper of OpenCV which can be compiled in Mono to be run on Windows, Linus, Mac OS X, iOS, and Android.

A wrapper for the OpenCV project to be used with .NET applications.

A Computer Vision library that combines OpenCV and NDArray together in .NET Standard. Great for processing images and video before feeding into a machine learning model.

ALGLIB is a cross-platform numerical analysis and data processing library. It supports several programming languages (C++, C#, Delphi) and several operating systems (Windows and POSIX, including Linux).

set of tools for engineers and scientists based on modern software frameworks that help to develop, deploy and maintain technical applications. Consist of development tooling (integrated in Visual Studio), visualization engine and a compute engine.

Filtering aims to provide a toolkit for digital signal processing, offering an infrastructure for digital filter design, applying those filters to data streams using data converters, as well as digital signal generators.

Math.NET Numerics aims to provide methods and algorithms for numerical computations in science, engineering and every day use. Covered topics include special functions, linear algebra, probability models, random numbers, interpolation, integration, regression, optimization problems and more.

Math.NET Spatial is aiming to become a geometry library for .Net and Mono.

Math.NET Symbolics is a basic open source computer algebra library for .Net and Mono written in F#.

Jupyter notebooks on Azure have builtin supper for F#.

Binder is an online application that allows you to point it to a github repo and start a Jupyter notebook that is in there. Binder supports C#, F# and even Powershell notebooks. And if you just want to try some notebooks right now, click

F# for Jupyter Notebooks with intellisense and integrated NuGet support.

.NET Interactive takes the power of .NET and embeds it into your interactive experiences. Share code, explore data, write, and learn across your apps in ways you couldn't before. Right now there is support for Jupyter, nteract, and Visual Studio Code.

BotSharp is an open source machine learning framework for AI Bot platform builder. This project involves natural language understanding, computer vision and audio processing technologies, and aims to promote the development and application of intelligent robot assistants in information systems.

Lean Engine is an open-source algorithmic trading engine built for easy strategy research, backtesting and live trading.

Automatic Differentiation tool targeted at Quantitative Finance.

A full port of Stanford NLP packages to .NET and also available precompiled as a NuGet package.

FsShelter is a library for defining and running Apache Storm topologies in F# using statically typed streams.

The .NET bindings for Spark are written on the Spark interop layer, designed to provide high performance bindings to multiple languages. .NET for Apache Spark is compliant with .NET Standard—a formal specification of .NET APIs that are common across .NET implementations. This means you can use .NET for Apache Spark anywhere you write .NET code.

Data frame and (time) series library for exploratory data manipulation with C# and F# support

Robust and extensible types and algorithms for manipulating structured data that supports aggregations, statistical funtions, sorting, grouping, joins, merges, handling missing values and more. Currently in preview mode.

Pandas ported to C#. It is a data analysis tool that can process multi-dimensional arrays using DataFrames.

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.