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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Machine learning for C# .Net
| Date | Stars |
|---|---|
| 2026-07-31 | 411 |
| 2026-08-06 | 411 |
| 2026-08-07 | 411 |
| 2026-08-14 | 411 |
| 2026-08-15 | 411 |
| 2026-08-18 | 412 |
| 2026-08-19 | 413 |
| 2026-08-22 | 413 |
| 2026-09-01 | 411 |
| 2026-09-20 | 411 |
Today
— stars today
This week
— stars this week
This month
-2 stars this month
Momentum
0.0
growth rate 0.00%/day
[](https://github.com/mdabros/SharpLearning/actions/workflows/dotnet.yml)
[](https://www.nuget.org/packages/SharpLearning.Containers/)
[](https://www.nuget.org/packages/SharpLearning.Containers)
[](https://github.com/mdabros/SharpLearning/blob/master/LICENSE)
SharpLearning
=================
SharpLearning is an opensource machine learning library for C# .Net.
The goal of SharpLearning is to provide .Net developers with easy access to machine learning algorithms and models.
Currently the main focus is supervised learning for classification and regression,
while also providing the necesarry tools for optimizing and validating the trained models.
SharpLearning provides a simple high-level interface for machine learning algorithms.
In SharpLearning a machine learning algorithm is refered to as a *Learner*,
and a machine learning model is refered to as a *PredictorModel*. An example of usage can be seen below:
```c#
// Create a random forest learner for classification with 100 trees
var learner = new ClassificationRandomForestLearner(trees: 100);
// learn the model
var model = learner.Learn(observations, targets);
// use the model for predicting new observations
var predictions = model.Predict(testObservations);
// save the model for use with another application
model.Save(() => new StreamWriter("randomforest.xml"));
```
All machine learning algorithms and models implement the same interface for easy replacement.
Currently SharpLearning supports the following machine learning algorithms and models:
* DecisionTrees
* Adaboost (trees)
* GradientBoost (trees)
* RandomForest
* ExtraTrees
* NeuralNets (layers for fully connected and convolutional nets)
* Ensemble Learning
All the machine learning algorithms have sensible default hyperparameters for easy usage.
However, several optimization methods are available for hyperparameter tuning:
* GridSearch
* RandomSearch
* ParticleSwarm
* GlobalizedBoundedNelderMead
* Hyperband
* BayesianOptimization
License
-------
SharpLearning is covered under the terms of the [MIT](LICENSE) license. You may therefore link to it and use it in both opensource and proprietary software projects.
Documentation
-------------
SharpLearning contains xml documentation to help guide the user while using the library.
Code examples and more information about how to use SharpLearning can be found in [SharpLearning.Examples](https://github.com/mdabros/SharpLearning.Examples)
The wiki also contains a set of guides on how to get started:
- [**Getting Started**](https://github.com/mdabros/SharpLearning/wiki/Getting-started).
- [**Introduction to SharpLearning**](https://github.com/mdabros/SharpLearning/wiki/Introduction-to-SharpLearning).
- [**Tuning Hyperparameters**](https://github.com/mdabros/SharpLearning/wiki/hyperparameter-tuning).
- [**Using SharpLearning.XGBoost**](https://github.com/mdabros/SharpLearning/wiki/Using-SharpLearning.XGBoost)
Installation
------------
The recommended way to get SharpLearning is to use NuGet. The packages are provided and maintained in the public [NuGet Gallery](https://nuget.org/profiles/mdabros/).
More information can be found in the [getting started](https://github.com/mdabros/SharpLearning/wiki/Getting-started) guide on the wiki
Learner and model packages:
- **SharpLearning.DecisionTrees** - Provides learning algorithms and models for DecisionTree regression and classification.
- **SharpLearning.AdaBoost** - Provides learning algorithms and models for AdaBoost regression and classification.
- **SharpLearning.RandomForest** - Provides learning algorithms and models for RandomForest and ExtraTrees regression and classificatExcerpt of 5,972 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:eabf6567f2d1b2c1, llm:Repository description and README: 'SharpLearning is an opensource machine learning library for C# .Net... main focus is supervised learning for classification and regression' plus topics: adaboost, decision-trees, ensemble-learning, gradient-boosting-machine, neural-nets, random-forest, machine-learning, csharp, dotnet.
matched fp:eabf6567f2d1b2c1, llm:Repository description and README: 'SharpLearning is an opensource machine learning library for C# .Net... main focus is supervised learning for classification and regression' plus topics: adaboost, decision-trees, ensemble-learning, gradient-boosting-machine, neural-nets, random-forest, machine-learning, csharp, dotnet.