Top 20 NuGet learning Packages

Provides learning algorithms and models for GradientBoost regression and classification.

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

ML.NET component for Image support

Microsoft.ML.TensorFlow contains ML.NET integration of TensorFlow.

Extreme Optimization Numerical Libraries for .NET P/Invoke MKL Provider for Windows.

Extreme Optimization Numerical Libraries for .NET Single-Precision P/Invoke MKL Provider for Windows.

This package contains native shared library artifacts for all supported platforms of ONNX Runtime.

An integration package for ML.NET models on scalable web apps and services.

C# bindings for Keras on OSX - Keras.NET is a high-level neural networks API, capable of running on top of TensorFlow, CNTK, or Theano.

C# bindings for Keras on Linux - Keras.NET is a high-level neural networks API, capable of running on top of TensorFlow, CNTK, or Theano.

LibTopoART provides C# implementations of several neural networks suited to problems such as online-learning, lifelong learning from data streams, as well as incremental learning and prediction from non-stationary data, noisy data, imbalanced data, and incomplete data.

C# Binding for the Apache MxNet library. NDArray, Symbolic and Gluon Supported MxNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dep...

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

DiffSharp is a tensor library with support for differentiable programming. It is designed for use in machine learning, probabilistic programming, optimization and other domains. For documentation and installation instructions visit: https://diffsharp.github.io/

DiffSharp is a tensor library with support for differentiable programming. It is designed for use in machine learning, probabilistic programming, optimization and other domains. For documentation and installation instructions visit: https://diffsharp.github.io/

DiffSharp is a tensor library with support for differentiable programming. It is designed for use in machine learning, probabilistic programming, optimization and other domains. For documentation and installation instructions visit: https://diffsharp.github.io/

DiffSharp is a tensor library with support for differentiable programming. It is designed for use in machine learning, probabilistic programming, optimization and other domains. For documentation and installation instructions visit: https://diffsharp.github.io/

DiffSharp is a tensor library with support for differentiable programming. It is designed for use in machine learning, probabilistic programming, optimization and other domains. For documentation and installation instructions visit: https://diffsharp.github.io/

ML.NET AutoML: Optimizes an ML pipeline for your dataset, by automatically locating the best feature engineering, model, and hyperparameters

Provides learning algorithms and models for AdaBoost regression and classification.