Machine Learning

The IBM.WatsonDeveloperCloud is a core project of Watson SDK in .Net. The other Watson SDK in .Net service packages depend on this package.

Provides learning algorithms and models for DecisionTree regression and classification.

Contains the IDataView system which is a set of interfaces and components that provide efficient, compositional processing of schematized data for machine learning and advanced analytics applications.

This package contains libraries of the Accord.NET Framework that are NOT available under the LGPL. It currently includes Accord.MachineLearning.GPL, which contains GPL code and thus can only be used inside GPL-compliant applications. Please take extra care before including this library in your projects.

Provides classification, regression, impurity and ranking metrics.

Process, transforms, filters and handle audio signals for machine learning and statistical applications. This package is part of the Accord.NET Framework.

Encog is an advanced machine learning framework that supports a variety of advanced algorithms, as well as support classes to normalize and process data. Machine learning algorithms such as Support Vector Machines, Artificial Neural Networks, Genetic Programming, Bayesian Networks, Hidden Markov Models and Genetic Algorithms are supported. Most Encog training algoritms are multi-threaded and scale well to multicore hardware. Encog can also make use of a GPU to further speed processing time. A GUI based workbench is also provided to help model and train machine learning algorithms. Encog has been in active development since 2008.

Microsoft.ML.TensorFlow.Redist contains the TensorFlow C library version 1.13.1 redistributed as a NuGet package.

Provides cross-validation, training/test set samplers and learning curves for SharpLearning.

TorchSharp makes PyTorch available for .NET users. libtorch-cpu-win-x64 contains components of the PyTorch LibTorch library version 2.5.1 redistributed as a NuGet package with added support for TorchSharp.

Provides learning algorithms and models for RandomForest and ExtraTrees regression and classification.

This is a C# class library containing some very basic geodesic algorithms. It is based on work by Mike Gavaghan and has been enhanced by me to cover also some variants of Mercators projection of the earth to flat maps. I cover Spherical and Elliptical Mercator projections, mapping the earth to a single flat map. I also handle the Universal Transverse Mercator (UTM) projection, dividing the earth into smaller grids which are then each mapped to a flat map. Finally - based on UTM - I implement an algorithm to put a finer grain mesh over the mapped area of the earth to be able to classify a geo-location by a discrete globally unique mesh number. This is done in order to facilitacte the application of some discrete algorithms - especially in the area of machine learning - on geo locations.

Provides optimization algorithms.

TorchSharp makes PyTorch available for .NET users. libtorch-cpu contains components of the PyTorch LibTorch library version 2.5.1 redistributed as a NuGet package with added support for TorchSharp.

TorchSharp makes PyTorch available for .NET users. libtorch-cpu-linux-x64 contains components of the PyTorch LibTorch library version 2.5.1 redistributed as a NuGet package with added support for TorchSharp.

TorchSharp makes PyTorch available for .NET users. libtorch-cpu-osx-x64 contains components of the PyTorch LibTorch library version 2.2.1 redistributed as a NuGet package with added support for TorchSharp.

ML.NET component for Microsoft.ML.Scoring library

Provides learning algorithms and models for GradientBoost regression and classification.

Botsharp.NLP is a set of tools for building C# programs to work with human language data. It can be used in common tasks like POS, NER and text classification in the NLP or NLU field. BotSharp.NLP has implemented below machine learning algorithms: Conditional Random Field (CRF) Support Vector Machine (SVM) N-Gram Tagger Regex Tokenizer Naive Bayes Classifier

TorchSharp makes PyTorch available for .NET users. libtorch-cuda-11.1-win-x64 contains components of the PyTorch LibTorch library version 1.9.0 redistributed as a NuGet package with added support for TorchSharp.

TorchSharp makes PyTorch available for .NET users. libtorch-cuda-11.3-win-x64 contains components of the PyTorch LibTorch library version 1.11.0 redistributed as a NuGet package with added support for TorchSharp.

TorchSharp makes PyTorch available for .NET users. libtorch-cuda-10.2-linux-x64 contains components of the PyTorch LibTorch library version 1.7.0 redistributed as a NuGet package with added support for TorchSharp.

Machine Learning library in .NET Core.

IBM.WatsonDeveloperCloud.Conversation.v1 wraps the Watson Developer Cloud Conversation service (http://www.ibm.com/watson/developercloud/conversation.html)

IBM.WatsonDeveloperCloud.SpeechToText.v1 wraps the Watson Developer Cloud Speech To Text service (http://www.ibm.com/watson/developercloud/speech-to-text.html)

TorchSharp makes PyTorch available for .NET users. libtorch-cuda-11.3-linux-x64 contains components of the PyTorch LibTorch library version 1.11.0 redistributed as a NuGet package with added support for TorchSharp.

.Net wrapper for the XGBoost machine learning library. Targeted for x64 and supports .Net Framework versions 4.0 and above.

TorchSharp makes PyTorch available for .NET users. libtorch-cuda-11.1-linux-x64 contains components of the PyTorch LibTorch library version 1.9.0 redistributed as a NuGet package with added support for TorchSharp.

A lightweight, linq-friendly inference library for .NET

Microsoft Machine Learning Scoring library for deep learning model inference. Current version of the library supports inferencing on ONNX v1.3 and TensorFlow v1.10.0 models. The library supports CPU execution with MKL/MKLDNN acceleration. Also supports CUDA GPU devices. For CPU execution of ONNX models, no extra libraries are required. However for scoring TensorFlow models, the CUDA libraries are needed for both CPU and GPU execution. Download and install CUDA 9.2 toolkit, CUDNN and device drivers separately. This package provides a .Net standard 1.3 compatible module for maximum portability. Currently supported platforms include x64 CPU on Windows OS only.

TorchSharp makes PyTorch available for .NET users. libtorch-cuda-11.7-win-x64 contains components of the PyTorch LibTorch library version 2.0.1 redistributed as a NuGet package with added support for TorchSharp.

TorchSharp makes PyTorch available for .NET users. libtorch-cpu-macos-x64 contains components of the PyTorch LibTorch library version 1.9.0 redistributed as a NuGet package with added support for TorchSharp.

Provides learning algorithms and models for neural net regression and classification.

LIBMF, the core computation library for matrix factorization in ML.NET

Bright Wire is an open source machine learning library. Includes neural networks (feed forward, convolutional and recurrent), naive bayes, linear regression, decision trees, logistic regression, k-means clustering and dimensionality reduction.

Microsoft.ML.Dnn contains APIs to do high level DNN training such as image classification.

Amazon Machine Learning Client for the AWS SDK for C++. AWS SDK for C++ provides a modern C++ (version C++ 11 or later) interface for Amazon Web Services (AWS). It is meant to be performant and fully functioning with low- and high-level SDKs, while minimizing dependencies and providing platform portability (Windows, OSX, Linux, and mobile).

Provides learning algorithms and models for AdaBoost regression and classification.

Provides ensemble learning for regression and classification.

MLOps.NET is a data science tool to track and manage the lifecycle of a ML.NET machine learning model.

IBM.WatsonDeveloperCloud.VisualRecognition.v3 wraps the Watson Developer Cloud Visual Recognition service (http://www.ibm.com/watson/developercloud/visual-recognition.html)

HDBSCAN is a clustering algorithm developed by Campello, Moulavi, and Sander. It extends DBSCAN by converting it into a hierarchical clustering algorithm, and then using a technique to extract a flat clustering based in the stability of clusters.

IBM.WatsonDeveloperCloud.NaturalLanguageUnderstanding.v1 wraps the Watson Developer Cloud Natural Language Understanding service (http://www.ibm.com/watson/developercloud/natural-language-understanding.html)

The Collection of the Machine Learning related functionality for AspNetCore or GenericHost.

Portable Accord Machine Learning contains machine learning algorithms of the Accord.NET Framework for mobile and tablet devices.

This is an extremely-fast and easy to use KDTree written entirely in modern C#. This is the fastest and simplest to use KDTree that I have been able to find for .NET. The project has thorough documentation and is open-source. This module is a part of the larger Supercluster project. Tutorials and Wiki: https://github.com/MathFerret1013/Supercluster.KDTree/wiki MSDN Style Docs: http://mathferret1013.github.io/Supercluster.KDTree GitHub: https://github.com/MathFerret1013/Supercluster.KDTree

Coralogix SDK for .NET

IBM.WatsonDeveloperCloud.TextToSpeech.v1 wraps the Watson Developer Cloud Text To Speech service (http://www.ibm.com/watson/developercloud/text-to-speech.html)

ML.NET is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers.

IBM.WatsonDeveloperCloud.ToneAnalyzer.v3 wraps the Watson Developer Cloud Tone Analyzer service (http://www.ibm.com/watson/developercloud/tone-analyzer.html)