Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Your new Mentor for Data Science E-Learning. | ||||||||||
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Machine learning, computer vision, statistics and general scientific computing for .NET | ||||||||||
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A collection of scientific methods, processes, algorithms, and systems to build stories & models. This roadmap contains 16 Chapters, whether you are a fresher in the field or an experienced professional who wants to transition into Data Science & AI | ||||||||||
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Conformal_classification | 159 | 8 months ago | mit | Jupyter Notebook | ||||||
Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction). | ||||||||||
Ineuron Full Stack Data Science Assignments | 68 | a year ago | Jupyter Notebook | |||||||
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Image_completion | 20 | 4 years ago | gpl-3.0 | Python | ||||||
Image Completion is the task of filling missing parts of a given image with the help of information from the known parts of the image. This is an application that takes an image with a missing part as input and gives a completed image as the result. |
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 is based on the following pattern:
For more information, please see the getting started guide, and check the classfication wiki. Please do not hesitate to edit the wiki if you would like!
To install the framework in your application, please use NuGet. If you are on Visual Studio, right-click on the "References" item in your solution folder, and select "Manage NuGet Packages." Search for Accord.MachineLearning (or equivalently, Accord.Math, Accord.Statistics or Accord.Imaging depending on your initial goal) and select "Install."
If you would like to install the framework on Unity3D applications, download the "libsonly" compressed archive from the framework releases page. Navigate to the Releases/Mono folder, and copy the .dll files to the Plugins folder in your Unity project. Finally, find and add the System.ComponentModel.DataAnnotations.dll assembly that should be available from your system to the Plugin folders as well.
The framework comes with a wide range of sample applications to help get you started quickly. If you downloaded the framework sources or cloned the repository, open the Samples.sln solution file in the Samples folder.
Please download and install the following dependencies:
Then navigate to the Sources directory, and open the Accord.NET.sln solution file. Note: the solution includes F# unit test projects that can be disabled/unloaded from the solution in case you do not have support for F# tools in your version of Visual Studio.
Please download and install the following dependencies:
Then navigate to the Sources directory, and open the Accord.NET.sln solution file. Note: the solution includes F# unit test projects that can be disabled/unloaded from the solution in case you do not have support for F# tools in your version of Visual Studio.
# Install Mono
sudo apt-get install mono-complete monodevelop monodevelop-nunit git autoconf make
# Clone the repository
git clone https://github.com/accord-net/framework.git
# Enter the directory
cd framework
# Build the framework solution using Mono
./autogen.sh
make build
make samples
make test
# Install Mono
brew update
brew cask install mono-mdk pkg-config automake
# Clone the repository
git clone https://github.com/accord-net/framework.git
# Enter the directory
cd framework
# Set some environment variables with OSX-specific paths
export PKG_CONFIG_PATH=/Library/Frameworks/Mono.framework/Versions/Current/lib/pkgconfig/
export MONO=/Library/Frameworks/Mono.framework/Versions/Current/bin/mono
export XBUILD=/Library/Frameworks/Mono.framework/Versions/Current/bin/xbuild
# Build the framework solution using Mono
./autogen.sh
make build
make samples
make test
If you would like to contribute, please do so by helping us update the project's Wiki pages. While you could also make a donation through PayPal , Flattr
, or any of the cryptocurrencies shown below, as well as fill-in bug reports and contribute code in the form of pull requests, the most precious donation we could receive would be a bit of your time - please take some minutes to submit us more documentation examples to our Wiki pages 😉
Donate using cryptocurrencies:
BTC: 16EUrG7AqbhrAbgVA1J3m4udFm3XFUntDE
ETH: 0xc152EA8c985984C34C08b54201a632E98AE98e8F
LTC: LPkWpq1ChUXXxpHZwvKFicVeWSXKPtnaYC
Note: all donations are 100% invested towards improving the framework, including, but not limited to, the hiring of extra developers to work on issues currently present at the project's issue tracker. If you would like to donate resources towards the development of a particular issue, please let us know!
Join the chat at https://gitter.im/accord-net/framework - but to have issues and questions answered, post it as an issue.