ankitasthana

3 ms·

1. 1.▲
   by ankitasthana 8y ago

   Here is an example of anomaly detection with ML.NET its not online learning based however. https://blogs.msdn.microsoft.com/dotnet/2018/11/08/announcin...
2. 2.▲
   by ankitasthana 8y ago

   The base ML.NET framework comes with a variety of numpy alternatives, datatypes for heterogenous (IDataView) and homogenous (VBuffer) data, a variety of transforms for data pre-processing, feature extraction (e.g. OneHotCodeEncoding), Missi
3. 3.▲
   by ankitasthana 8y ago

   The way to think about ML.NET is really a higher level framework which comes built in with traditional ML trainers, transforms etc. and through its extensibility allows .NET developers to also use other leading frameworks for deep learning
4. 4.▲
   by ankitasthana 8y ago

   ML.NET is really a framework which provides ability to build custom traditional machine learning algorithms for a number of scenarios like (classification, regression, recommendation etc.). ML.NET is also extensible, which means we can abso
5. 5.▲
   by ankitasthana 8y ago

   ML.NET is a machine learning framework which is aimed at providing the E2E workflow for infusing ML into .NET apps across pre-processing, feature engineering, modeling, evaluation, and model consumption. The base framework comes with a vari