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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 ...
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.
Templates are pages that are embedded (transcluded) into other pages to allow for the repetition of information. Wikipedia:List of infoboxes for infoboxes, which are small panels that summarize key features of the page's subject. Wikipedia:Requested templates, to request creation of a template. Use this form to search in the Template: or ...
In the field of electronics, a technique where part of the output of a system is used at startup can be described as bootstrapping. A bootstrap circuit is one where part of the output of an amplifier stage is applied to the input, so as to alter the input impedance of the amplifier. When applied deliberately, the intention is usually to ...
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Bootstrap Productions. Tyler Doherty & Derek Fenner, 2006. Bootstrap Productions is a nonprofit collaborative arts and literary organization based in Lowell, Massachusetts, [1] [2] which is primarily known for its publishing arm, Bootstrap Press, a small-press publisher of contemporary experimental writing. [3]
Bootstrapping (compilers), the process of writing a compiler in the programming language it is intended to compile. Bootstrapping (electronics), a type of circuit that employs positive feedback. Bootstrapping (finance), a method for constructing a yield curve from the prices of coupon-bearing products. Bootstrapping (law), a former rule of ...
Artificial intelligence and machine learning. Bootstrapping is a technique used to iteratively improve a classifier 's performance. Typically, multiple classifiers will be trained on different sets of the input data, and on prediction tasks the output of the different classifiers will be combined.
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