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A tag is a keyword or label that categorizes your question with other, similar questions. Using the right tags makes it easier for others to find and answer your question.
Django-file-upload refers to how django handles file uploads

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In a relational database management system (RDBMS) a table organizes the information in rows and columns. Each table has a defined number of columns, each of which is identified by its name, but the number of rows is variable, consisting of one for each line of data.

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All questions concerning numbers which support extremely high precision: Libraries in programming languages (GMP, MPFR), support of arbitrary precision in computer algebra systems (CAS, Mathematica, Maple, Mathlab) and how to correctly use and calculate numbers with very high precision and accuracy.

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A tablespace is a database concept and refers to a storage location where the actual data underlying database objects can be kept.

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In JavaScript, event delegation is a concept of listening to specific events on some element (eg. the whole document) to catch these events being fired on any of the child elements, even the ones that did not exist when the event was being attached. This happens thanks to another concept called "event bubbling", which makes events to be visible by parent elements. Within every event handler it is possible to check what was the target element of the event.

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Do not use this tag as it is ambiguous.

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A Sass-powered version of Bootstrap, ready to drop right into your Sass powered applications.

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Goutte is a simple headless web browser, written in PHP.

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In Java, an action event is a semantic event which indicates occurrence of an action generated by a component like a button. This action event then passes to its ActionListener object, which is done by calling addActionListener method of the component.

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WeChat is a mobile text and voice messaging communication service introduced by Tencent Inc., one of China's largest Internet service providers, in January 2011. It's a free instant messaging service application.

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SHAP (SHapley Additive exPlanations) is a unified approach to explain the output of any machine learning model. SHAP connects game theory with local explanations, uniting several previous methods and representing the only possible consistent and locally accurate additive feature attribution method based on expectations (see the SHAP NIPS paper for details).

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