Top 20 NuGet learning Packages
ML.NET is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers.
A complete C# re-write of Berkeley's open source Convolutional Architecture for Fast Feature Encoding (CAFFE) for Windows C# Developers with full On-line Help, now with Policy Gradient Reinforcement Learning, cuDNN LSTM Recurrent Learning, and Neural Style Transfer support!
Provides Machine Learning WebServices management capabilities for Microsoft Azure.
Provides developers with a library to create and manage machine learning compute resources.
Microsoft.ML.TensorFlow.Redist contains the TensorFlow C library version 1.13.1 redistributed as a NuGet package.
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.
The Extreme Optimization Numerical Libraries for .NET are a set of libraries for numerical computing and data analysis. This is the main package that contains all the core functionality. For optimal performance, we strongly recommend also referencing one of the native packages based on Intel's Mat...
An internal deployment package that allows for Algorithmia algorithm .net support
WeightedSelector.NET lets you assign weights to a set of choices, then make intelligent decisions based on each choice's proportion of the total weight. This simple concept is useful for machine learning scenarios where choices need to be made based on dynamic factors. Great examples include sugg...
The core of the Accord.NET Framework. Contains basic classes such as general exceptions and extensions used by other framework libraries.
Contains 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.
Developing a C# wrapper to help developer easily create and train deep neural network models. Easy to use library, just focus on research Multiple backend - ArrayFire (In Progress), TensorSharp (In Progress), CNTK (Not Started), TensorFlow (Not Started), MxNet (Not Started) CUDA/ OpenCL support for...