Showing 4 open source projects for "make human"

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  • 1
    autoresearch

    autoresearch

    AI agents autonomously run and improve ML experiments overnight

    ...Designed to run on a single GPU, it keeps the research loop minimal and self-contained to make autonomous experimentation practical. Over time, the agent logs experiments, evaluates improvements, and gradually evolves the model through automated trial-and-error.
    Downloads: 4 This Week
    Last Update:
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  • 2
    Interpretable machine learning

    Interpretable machine learning

    Book about interpretable machine learning

    ...Did it learn generalizable features? Or are there some odd artifacts in the training data which the algorithm picked up? This book will give an overview over techniques that can be used to make black boxes as transparent as possible and explain decisions. In the first chapter algorithms that produce simple, interpretable models are introduced together with instructions how to interpret the output. The later chapters focus on analyzing complex models and their decisions. In an ideal future, machines will be able to explain their decisions and make a transition into an algorithmic age more human.
    Downloads: 3 This Week
    Last Update:
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  • 3
    Yellowbrick

    Yellowbrick

    Visual analysis and diagnostic tools to facilitate ML selection

    Yellowbrick extends the Scikit-Learn API to make model selection and hyperparameter tuning easier. Under the hood, it’s using Matplotlib. Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the scikit-learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines scikit-learn with matplotlib in the best tradition of the scikit-learn documentation, but to produce visualizations for your machine learning workflow.
    Downloads: 0 This Week
    Last Update:
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  • 4
    HoldemAI

    HoldemAI

    Texas Holdem Poker AI

    ...The AI uses players' betting actions to calculate a probability distribution of their hole cards and uses it to evaluate hand strength and the best possible action. Small random changes are made to mimic human behavior and make the AI less predictable. Future versions will include adaptive opponent modeling using neural networks to improve the AI's strength. The AI code can be easily adapted for input from screen scrapers.
    Downloads: 0 This Week
    Last Update:
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