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  2. Template:If - Wikipedia

    en.wikipedia.org/wiki/Template:If

    Each nested use adds 5 levels to the template expansion depth, so 7 nested if-templates would use 35 levels (5*7) of the 41-level limit. Using P-if syntax: A similar if-structure can be coded without Template:If, by using the {} and {} templates in a "P-if" structure. Template:P1 always returns parameter 1, and P2 returns the 2nd. So, a ...

  3. Power Query - Wikipedia

    en.wikipedia.org/wiki/Power_Query

    Power Query was first announced in 2011 under the codename "Data Explorer" as part of Azure SQL Labs. In 2013, in order to expand on the self-service business intelligence capabilities of Microsoft Excel, the project was redesigned to be packaged as an add-in Excel and was renamed "Data Explorer Preview for Excel", [4] and was made available for Excel 2010 and Excel 2013. [5]

  4. Template:Iferror then show/doc - Wikipedia

    en.wikipedia.org/wiki/Template:Iferror_then_show/doc

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us

  5. Help:Conditional expressions - Wikipedia

    en.wikipedia.org/wiki/Help:Conditional_expressions

    See also: the {{}} template. The #if function selects one of two alternatives based on the truth value of a test string. {{#if: test string | value if true | value if false}} As explained above, a string is considered true if it contains at least one non-whitespace character.

  6. Résumé - Wikipedia

    en.wikipedia.org/wiki/Résumé

    A résumé or resume (or alternatively resumé), [a] [1] is a document created and used by a person to present their background, skills, and accomplishments. Résumés can be used for a variety of reasons, but most often are used to secure new jobs, whether in the same organization or another.

  7. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    The word with embeddings most similar to the topic vector might be assigned as the topic's title, whereas far away word embeddings may be considered unrelated. As opposed to other topic models such as LDA , top2vec provides canonical ‘distance’ metrics between two topics, or between a topic and another embeddings (word, document, or otherwise).

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