Showing 4 open source projects for "state machine"

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  • 1
    Roadmap To Learn Generative AI In 2025

    Roadmap To Learn Generative AI In 2025

    Basic Machine Learning Natural Language Processing Roadmap

    Roadmap To Learn Generative AI In 2025 is a curated learning path focused on contemporary generative AI — covering large language models (LLMs), diffusion-based image generation, prompt engineering, multi-modal AI, fine-tuning techniques, and the practical considerations for deploying generative models. It’s aimed at learners and developers who already have some programming or ML basics and wish to specialize in generative AI, offering a modern, structured plan that reflects the state of the...
    Downloads: 0 This Week
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  • 2
    JQM Java Quine McCluskey

    JQM Java Quine McCluskey

    JQM - Java Quine McCluskey for minimization of Boolean functions.

    Java Quine McCluskey (JQM) implements the Quine-McCluskey algorithm with Petrick’s Method for minimizing Boolean functions. Designed for both education and industrial application, it handles up to 16 variables and functions. Uniquely, JQM bridges the gap between theory and practice: it visualizes the solution process with generated Karnaugh Maps for students, while supporting PLC engineers by exporting results to Structured Text (ST) and Ladder Diagram (LD). The software includes a GUI for...
    Downloads: 1 This Week
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  • 3
    A collection of open source software and documents on machine perception and machine learning. Includes a state of the art face detector (MPISearch), video labeling tools (Score), and tutorials (Kolmogorov Tutorials).
    Downloads: 0 This Week
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  • 4
    DBNL

    DBNL

    Dynamic Bayesian Network Library

    DBNL is a cross-platform library that offers a variety of implementations of Bayesian networks and machine learning algorithms. It is a flexible library that covers all aspects of Bayesian netwoks from representation to reasoning and learning. It allows you to create simple static networks as well as complex temporal models with changing structure. It can handle highly non-linear dependencies between multivariate random variables. The particle based inference can answer arbitrary...
    Downloads: 0 This Week
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