Showing 5 open source projects for "spreadsheet machine learning"

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  • 1
    Accord.NET Framework

    Accord.NET Framework

    Scientific computing, machine learning and computer vision for .NET

    The Accord.NET Framework provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. The project is compatible with the .NET Framework. NET Standard, .NET Core, and Mono.
    Downloads: 1 This Week
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  • 2
    Michel Max Spread - XLSX Loader/Creator

    Michel Max Spread - XLSX Loader/Creator

    Michel Max MMSpread is an API XLSX files without the MS Excel ins...

    The Michel Max - MMSpread is an API to generate XLSX Spreadsheets without having the Microsoft Excel installed in the Machine. It allows you to Load XLSX files and edit them, or even create them from nothing, without having annoying alerts when it is open. It supports the following Microsoft Excel items: - All styles (font, border, background, etc). - Validation: List ONLY, and it has to be sequential. - Conditional Formula Formatting. - Comments. - Number Format. Extra...
    Downloads: 0 This Week
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  • 3
    neural network designer

    neural network designer

    a dbms for neural nets. Chatbots, DTrees, random forests, n-grams,...

    This project consists out of a windows based designer application and a library (that can run on multiple platforms, including android) together with several demo applications (including an MVC3 chatbot client and an android application). It is probably best compared to a database management system, but for neural networks instead of relational data. As such, the library is optimized for handling any type of data-size by using advanced streaming and caching algorithms. With the designer,...
    Downloads: 0 This Week
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  • 4
    A C# library for use in image processing and computer vision research.
    Downloads: 0 This Week
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  • 5
    OpenSkyNet - Moving towards a comprehensive artificial intelligence solution for game developers under the LGPL. The goals are to implement action selection solvers, robust steering behaviors (including pathfinding algorithms), and machine learning.
    Downloads: 0 This Week
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