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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.
Bootstrap aggregating, also called bagging (from bootstrap aggregating) or bootstrapping, is a machine learning (ML) ensemble meta-algorithm designed to improve the stability and accuracy of ML classification and regression algorithms.
W3Schools is a freemium educational website for learning coding online. [1] [2] Initially released in 1998, it derives its name from the World Wide Web but is not affiliated with the W3 Consortium. [3] [4] [unreliable source] W3Schools offers courses covering many aspects of web development. [5] W3Schools also publishes free HTML templates.
In general, bootstrapping usually refers to a self-starting process that is supposed to continue or grow without external input. Many analytical techniques are often called bootstrap methods in reference to their self-starting or self-supporting implementation, such as bootstrapping (statistics), bootstrapping (finance), or bootstrapping (linguistics).
Bootstrap Studio was launched on October 19, 2015 with a post on Product Hunt where it reached number 4 in the Product of the Day category. [5] Version 2.0 of the software was released on January 22, 2016 and brought JavaScript editing, multi-page support and improved the CSS support. [6] Version 4.0 was launched on November 1, 2017.
A bootstrapping node, also known as a rendezvous host, [1] is a node in an overlay network that provides initial configuration information to newly joining nodes so that they may successfully join the overlay network.
Boost is a set of libraries for the C++ programming language that provides support for tasks and structures such as linear algebra, pseudorandom number generation, multithreading, image processing, regular expressions, and unit testing. It contains 164 individual libraries (as of version 1.76).
Soon after the introduction of gradient boosting, Friedman proposed a minor modification to the algorithm, motivated by Breiman's bootstrap aggregation ("bagging") method. [2] Specifically, he proposed that at each iteration of the algorithm, a base learner should be fit on a subsample of the training set drawn at random without replacement. [ 10 ]