Showing 4 open source projects for "multi-system"

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  • 1
    BotSharp

    BotSharp

    AI Multi-Agent Framework in .NET

    ...It's written in C# running on .Net Core that is full cross-platform framework. C# is a enterprise-grade programming language which is widely used to code business logic in information management-related system.
    Downloads: 1 This Week
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  • 2
    Glint Translator
    Glint Translator is a high-performance, privacy-focused Windows application for real-time in-game and voice translation without interrupting gameplay. Powered by leading cloud and offline/local AI models including Google Gemini, OpenAI, xAI Grok, DeepL, Azure, and Ollama (Gemma, Qwen), it seamlessly translates 240+ languages with an intuitive, plug-and-play interface. Example: They speak German → you see Turkish They speak Turkish → you see German 🧠 AI Model Support Google Gemini:...
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    Downloads: 24 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, you are able to create different types of decision trees, random forests, n-grams, pattern-matchers, conversational agents and all sorts of AI related algorithms. ...
    Downloads: 0 This Week
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  • 4
    CRFSharp

    CRFSharp

    CRFSharp is a .NET(C#) implementation of Conditional Random Field

    ...Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. Currently, when training corpus, compared with CRF++, CRF# can make full use of multi-core CPUs and only uses very low memory, and memory grow is very smoothly and slowly while amount of training corpus, tags increase. with multi-threads process, CRF# is more suitable for large data and tags training than CRF++ now. For example, in machine with 64GB, CRF# encodes model with more than 4.5 hundred million features quickly.
    Downloads: 0 This Week
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