15 Machine Learning and Data Science packages and projects
9.7 9.0 C#Cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers.
9.5 6.7 L2 C#Machine learning framework combined with audio and image processing libraries (computer vision, computer audition, signal processing and statistics).
7.1 1.9 L2 C#Framework for developers and researchers in the fields of Computer Vision and Artificial Intelligence (image processing, neural networks, genetic algorithms, machine learning, robotics).
6.4 5.1 HTMLF# type providers for accessing XML, JSON, CSV and HTML files (based on sample documents) and for accessing WorldBank data
6.3 5.8 F#Data frame and (time) series library for exploratory data manipulation with C# and F# support
6.1 0.0 L5 C#Advanced image processing and computer vision algorithms made as fluent extensions.
5.9 0.0 C#Encog Machine Learning Framework
5.6 0.0 L3 C#Designed to include the most popular supervised and unsupervised learning algorithms while minimizing the friction involved with creating the predictive models.
4.6 6.4 C#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.
4.2 0.0 F#Type provider that exposes R packages and functions in a type-safe way to F# callers
3.6 7.8 C#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
1.9 5.6 F#An in-memory neural network library written in F#.
1.8 0.0 MatlabA framework for running Bayesian inference in graphical models. It can also be used for probabilistic programming. [Proprietary] [Free] [Research]
1.5 0.0A collection of data science and machine learning libraries for F# and .NET
- -A rich machine learning ecosystem for .NET created by porting the most popular Python libraries to C#.
Scout APM uses tracing logic that ties bottlenecks to source code so you know the exact line of code causing performance issues and can get back to building a great product faster.
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