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Back-end (Server-side) table in most popular websites Websites C# C C++ D Elixir Erlang Go Hack Haskell Java JavaScript Perl PHP Python Ruby Scala; Google: No Yes Yes No No No Yes No No Yes Yes No No Yes No No Facebook: No No Yes Yes No Yes No Yes Yes Yes No No No Yes No No YouTube: No Yes Yes No No No Yes No No Yes No No No Yes No No Yahoo: No ...
In software development, frontend refers to the presentation layer that users interact with, while backend involves the data management and processing behind the scenes. In the client–server model , the client is usually considered the frontend, handling user-facing tasks, and the server is the backend, managing data and logic.
Node.js (JavaScript): While JavaScript is traditionally a client-side language, Node.js enables developers to run JavaScript on the server side. It is known for its event-driven, non-blocking I/O model , making it suitable for building scalable and high-performance applications.
YouTube was founded as a video sharing platform in 2005 and is now the most visited website in the US as of 2019. [1] Almost immediately after the site's launch, educational institutions, such as MIT OpenCourseWare and TED, were using it for the distribution of their content.
Open Blocks visual programming is closely related to StarLogo TNG, a project of STEP, and Scratch, a project of the MIT Media Lab's Lifelong Kindergarten Group led by Mitchel Resnick. App Inventor 2 [ 5 ] replaced Open Blocks with Blockly , a blocks editor that runs within a web browser .
February 2015) (Learn how and when to remove this message) ( Learn how and when to remove this message ) In computer science , declarative programming is a programming paradigm —a style of building the structure and elements of computer programs—that expresses the logic of a computation without describing its control flow .
Google Cloud Platform (GCP) is a suite of cloud computing services offered by Google that provides a series of modular cloud services including computing, data storage, data analytics, and machine learning, alongside a set of management tools. [5]
Neural networks are typically trained through empirical risk minimization.This method is based on the idea of optimizing the network's parameters to minimize the difference, or empirical risk, between the predicted output and the actual target values in a given dataset. [4]