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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.
Dashboards aid with budgeting, management control, and wage control. Dashboards are used to present data in a quick and easy to read way. The ability to present data in a quick way with a visual allows for more data to be processed and understood. Dashboards are used for performance reports, sales analysis on sectors, and inventory rotation.
The framework includes most common patterns needed to prototype a responsive site. Since version 2.0 it also supports responsive design . [ 6 ] This means the graphic design of web pages adjusts dynamically, taking into account the characteristics of the device used (PC, tablet, mobile phone).
These can be viewed as a ready-made web design, used to mass-produce "cookie-cutter" websites for rapid deployment. They also commonly include themes in place of CSS styles. In general, the template language is used only with the editor's software. [5] FrontPage and Dreamweaver were once the most popular editors with template sub-systems.
The Quasar Framework (commonly referred to as Quasar; pronounced / ˈ k w eɪ. z ɑːr / [4]) is an open-source Vue.js based framework for building apps with a single codebase. It can be deployed on the Web as a SPA, PWA, SSR, to a Mobile App, using Cordova for iOS & Android, and to a Desktop App, using Electron for Mac, Windows, and Linux.
This is an accepted version of this page This is the latest accepted revision, reviewed on 9 February 2025. High-level programming language Not to be confused with Java (programming language), Javanese script, or ECMAScript. JavaScript Screenshot of JavaScript source code Paradigm Multi-paradigm: event-driven, functional, imperative, procedural, object-oriented Designed by Brendan Eich of ...
The bootstrap sample is taken from the original by using sampling with replacement (e.g. we might 'resample' 5 times from [1,2,3,4,5] and get [2,5,4,4,1]), so, assuming N is sufficiently large, for all practical purposes there is virtually zero probability that it will be identical to the original "real" sample. This process is repeated a large ...
[5] [43] Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a ...