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  2. Bootstrap (front-end framework) - Wikipedia

    en.wikipedia.org/wiki/Bootstrap_(front-end...

    Bootstrap (formerly Twitter Bootstrap) is a free and open-source CSS framework directed at responsive, mobile-first front-end web development. It contains HTML, CSS and (optionally) JavaScript -based design templates for typography, forms, buttons, navigation, and other interface components. As of May 2023, Bootstrap is the 17th most starred ...

  3. Bootstrapping (statistics) - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping_(statistics)

    Bootstrapping is any test or metric that uses random sampling with replacement (e.g. mimicking the sampling process), and falls under the broader class of resampling methods. Bootstrapping assigns measures of accuracy ( bias, variance, confidence intervals, prediction error, etc.) to sample estimates.

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  5. Bootstrapping (compilers) - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping_(compilers)

    Bootstrapping (compilers) In computer science, bootstrapping is the technique for producing a self-compiling compiler – that is, a compiler (or assembler) written in the source programming language that it intends to compile. An initial core version of the compiler (the bootstrap compiler) is generated in a different language (which could be ...

  6. Bootstrap Studio - Wikipedia

    en.wikipedia.org/wiki/Bootstrap_Studio

    Bootstrap Studio is a proprietary web design and development application. It offers a large number of components for building responsive pages including headers, footers, galleries and slideshows along with basic elements, such as spans and divs. The program can be used for building websites and prototypes.

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    Fast, secure and reliable email. Stay in touch and enjoy the ride with AOL Mail. Get started. System requirements: AOL Mail is free email with beautiful design & customer support, along with ...

  8. k-nearest neighbors algorithm - Wikipedia

    en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

    The training phase of the algorithm consists only of storing the feature vectors and class labels of the training samples. In the classification phase, k is a user-defined constant, and an unlabeled vector (a query or test point) is classified by assigning the label which is most frequent among the k training samples nearest to that query point.

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