Accord.NET - Machine learning, Computer vision, Statistics and general scientific computing for .NET

  •        183

The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

After merging with the AForge.NET project, the framework now offers a unified API for learning/training machine learning models that is both easy to use and extensible.

It supports Vector Machines , Logistic Regression , Decision Trees , Neural Networks , Deep Learning (Deep Neural Networks), Levenberg-Marquardt with Bayesian Regularization , Restricted Boltzmann Machines , Sequence classification , Hidden Markov Classifiers and Hidden Conditional Random Fields, Multiple linear regression , Multivariate linear regression , polynomial regression , logarithmic regression.

Clustering algorithms like K-Means , K-Modes , Mean-Shift , Gaussian Mixture Models , Binary Split, Image processing, Load, parse, save, filter and transform audio signals, Real-time face detection and tracking , as well as general methods for detecting , tracking and transforming objects in image streams and lot more.

http://accord-framework.net
https://github.com/accord-net/framework

Tags
Implementation
License
Platform

   




Related Projects

vs-mef - Managed Extensibility Framework (MEF) implementation used by Visual Studio


The MEF that ships with the .NET Framework (System.ComponentModel.Composition) is good, and Visual Studio used it through Dev12 (Visual Studio 2013). But it had performance limitations inherent in its "dynamic composition" capability, which Visual Studio did not require, and Visual Studio needed to surpass the performance that ".NET MEF" could offer.The .NET team went on to create an all new implementation of MEF, which was "portable", and shipped in a NuGet package called Microsoft.Composition. This was faster in some respects than the .NET Framework, but lacked the extensibility Visual Studio required, was incompatible with MEF parts written for ".NET MEF", and suffered from poor startup performance. This new MEF implementation was later renamed to System.Composition, but has otherwise not received much by way of upgrades.

Machine Learning Framework


Machine Learning Framework (MLF) is a library based on .NET Framework for machine learning implementation. This library consists of collection of machine learning algorithms such as Bayesian, Neural Network, SOM, Genetic Algorithm, SVM, and etc.

Jubatus - Framework and Library for Distributed Online Machine Learning


Jubatus is a distributed processing framework and streaming machine learning library. Jubatus includes these functionalities: Online Machine Learning Library: Classification, Regression, Recommendation (Nearest Neighbor Search), Graph Mining, Anomaly Detection, Clustering, Feature Vector Converter (fv_converter): Data Preprocess and Feature Extraction, Framework for Distributed Online Machine Learning with Fault Tolerance.

Epic-Framework - A framework for image processing and computer vision research


A framework for image processing and computer vision research

Caffe - Deep Learning Framework from Berkley Vision


Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.



Neural-Networks - A general neural network framework for various machine learning uses.


A general neural network framework for various machine learning uses.

Accord.NET Framework


Scientific computing, machine learning and computer vision framework.

Machine-Learning---SVMs - Machine Learning - Support Vector Machines (SVMs). Code in Torch/Lua.


Machine Learning - Support Vector Machines (SVMs). Code in Torch/Lua.

Visualization Toolkit


The Visualization Toolkit (VTK) is an open-source, freely available software system for 3D computer graphics, image processing and visualization. VTK supports a wide variety of visualization algorithms including: scalar, vector, tensor, texture, and volumetric methods; and advanced modeling techniques such as: implicit modeling, polygon reduction, mesh smoothing, cutting, contouring, and Delaunay triangulation.

vstest - Visual Studio Test Platform is the runner and engine that powers test explorer and vstest


The Visual Studio Test Platform is an open and extensible test platform that enables running tests, collect diagnostics data and report results. The Test Platform supports running tests written in various test frameworks, and using a pluggable adapter model. Based on user-choice, the desired test framework and its corresponding adapter can be acquired as a vsix or as NuGet package as the case may be. Adapters can be written in terms of a public API exposed by the Test Platform.The Test Platform currently ships as part Visual Studio 2017, and in the .NET Core Tools Preview 3.

ATF


Authoring Tools Framework (ATF) is a set of C#/.NET components for making tools on Windows. ATF has been in continuous development in Sony Computer Entertainment's (SCE) Worldwide Studios central tools group since early 2005. ATF has been used by most SCE first party studios to make many custom tools such as Naughty Dog’s level editor and shader editor for The Last of Us, Guerrilla Games’ sequence editor for Killzone games (including the Killzone: Shadow Fall PS4 launch title), an animation blen

seq2seq - A general-purpose encoder-decoder framework for Tensorflow


A general-purpose encoder-decoder framework for Tensorflow that can be used for Machine Translation, Text Summarization, Conversational Modeling, Image Captioning, and more.The official code used for the Massive Exploration of Neural Machine Translation Architectures paper.

ACS-Deployment-Tutorial - A tutorial on how to deploy a Dockerised deep learning application on Azure Container Services


Deploying machine learning models can often be tricky due to their numerous dependencies, deep learning models often even more so. One of the ways to overcome this is to use Docker containers. Unfortunately, it is rarely straight-forward. In this tutorial, we will demonstrate how to deploy a pre-trained deep learning model using Azure Container Services, which allows us to orchestrate a number of containers using DC/OS. By using Azure Container Services, we can ensure that it is performant, scalable and flexible enough to accommodate any deep learning framework. The Docker image we will be deploying can be found here. It contains a simple Flask web application with Nginx web server. The deep learning framework we will use is the Microsoft Cognitive Toolkit (CNTK) and we will be using a pre-trained model; specifically the ResNet 152 model.Azure Container Services enables you to configure, construct and manage a cluster of virtual machines pre-configured to run containerized applications. Once the cluster is set up you can use a number of open-source scheduling and orchestration tools, such as Kubernetes and DC/OS. This is ideal for machine learning application since we can use Docker containers which enable us to have ultimate flexibility in the libraries we use and allows us to easily scale up based on demand. While always ensuring that our application remains performant. You can create an ACS through the Azure portal but in this tutorial we will be constructing it using the Azure CLI.

Icarus Scene Engine 3.0


Icarus Scene Engine 3.0 is a cross-platform 3D framework, integrating open source APIs into a cohesive all-open-source, all .NET solution. For Windows, MacOSX, Linux, Web, iOS. Uses OpenTK, OpenGL, OpenAL, Mono/.NET, FFMpeg. Works with .NET 2.0 or later, using Visual Studio (inc. Express editions) as well as MonoDevelop amp; Xamarin Studio. NOTE: Icarus Scene Engine 3.0 is an almost total re-write from Version 2.0. More focussed, more specific in function and goals, much more developer-fri

OpenCV - Open Source Computer Vision


OpenCV (Open Source Computer Vision) is a library of programming functions for real time computer vision. The library has more than 500 optimized algorithms. It is used to interactive art, to mine inspection, stitching maps on the web on through advanced robotics.

multiverso - Parameter server framework for distributed machine learning


Multiverso is a parameter server based framework for training machine learning models on big data with numbers of machines. It is currently a standard C++ library and provides a series of friendly programming interfaces. With such easy-to-use APIs, machine learning researchers and practitioners do not need to worry about the system routine issues such as distributed model storage and operation, inter-process and inter-thread communication, multi-threading management, and so on. Instead, they are

nHydrate - Conceive, Model, Generate


nHydrate is an object-relational mapping (ORM) solution for the Microsoft .NET platform providing a framework for a relational database to be mapped to .NET objects. It is designed to alleviate the software developers experience writing persistence domains. The model controls database generation, LINQ syntax, API, DAL, etc.

OpenPR


OpenPR stands for Open Pattern Recognition project and is intended to be an open source library for algorithms of image processing, computer vision, natural language processing, pattern recognition, machine learning and the related fields.

gophernet - A simple from-scratch neural net written in Go


This is a simple neural network built in Go. It is further discussed here and in Machine Learning with Go.

vsts-dotnet-samples - C# samples that show integration with Visual Studio Team Services and Team Foundation Server


This repository contains C# samples that show how to integrate with Team Services and Team Foundation Server using our public client libraries, service hooks, and more.For .NET developers, the primary (and highly recommended) way to integrate with Team Services and Team Foundation Server is via our public .NET client libraries available on Nuget. Microsoft.TeamFoundationServer.Client is the most popular Nuget package and contains clients for interacting with work item tracking, Git, version control, build, release management and other services.