awesome-machine-master
github.com/webexfavorhero/awesome-machine-master ↗python exam
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Installation instructions →What's inside
Resources
- 2012-paper-diginorm
- Accord.MachineLearning
Support Vector Machines, Decision Trees, Naive Bayesian models, K-means, Gaussian Mixture models and general algorithms such as Ransac, Cross-validation and Grid-Search for machine-learning applications. This package is part of the Accord.NET Framework.
- Accord.NET
Together with AForge.NET, this library can provide image processing and computer vision algorithms to Windows, Windows RT and Windows Phone. Some components are also available for Java and Android.
- adam
A genomics processing engine and specialized file format built using Apache Avro, Apache Spark and Parquet. Apache 2 licensed.
- AForge.NET
Open source C# framework for developers and researchers in the fields of Computer Vision and Artificial Intelligence. Development has now shifted to GitHub.
- ahaz
ahaz: Regularization for semiparametric additive hazards regression
Showing a sample of 466 resources. View the full list on GitHub →