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Bootstrap is a free and open-source library that simplifies the creation of responsive, mobile-first web pages with HTML, CSS and JS. It provides design templates, components and utilities for typography, forms, navigation, and more.
Bootstrapping is a term that refers to various self-sustaining or self-improving processes that do not require external input. It can also be a metaphor for overcoming difficulties or achieving success by one's own efforts. Learn about the origin, applications and examples of bootstrapping in computing, software development and other fields.
Learn about the history and methods of booting, the process of loading software into memory and starting a computer. Find out how booting differs from rebooting, restarting, and hibernating, and what are the terms bootstrap and bootstrap loader.
A bootloader is a program that boots a computer by loading an operating system or other software from a storage device. Learn about the types, functions, and constraints of bootloaders, and see some common examples such as BIOS, UEFI, GRUB, and iBoot.
Operation Bootstrap was a series of projects that transformed Puerto Rico's economy from sugarcane plantations to an industrial and developed one. It offered tax incentives, labor pool, and emigration to attract U.S. investment, but also faced challenges such as unemployment and population decline.
Bootstrapping is a procedure for estimating the distribution of an estimator by resampling data or a model. Learn the history, approach, advantages, disadvantages and recommendations of bootstrapping in statistics.
Bootstrapping is a technique to increase the output or input impedance of a circuit by using part of the output as feedback. Learn how bootstrap capacitors are used in MOSFETs, switch-mode power supplies, AC amplifiers and digital integrated circuits.
Bootstrap aggregating, or bagging, is a machine learning ensemble meta-algorithm that improves stability and accuracy of classification and regression methods. It generates multiple training sets by sampling with replacement from the original dataset and combines the models by averaging or voting.