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  1. docs/de/docs/_llm-test.md

    * <abbr title="Eine Methode des Machine Learning, die künstliche neuronale Netze mit zahlreichen versteckten Schichten zwischen Eingabe- und Ausgabeschicht verwendet und so eine umfassende interne Struktur entwickelt">Deep Learning</abbr>
    
    ### Das abbr gibt eine vollständige Phrase und eine Erklärung { #the-abbr-gives-a-full-phrase-and-an-explanation }
    
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  2. docs/es/docs/async.md

    Eso, más el simple hecho de que Python es el lenguaje principal para **Data Science**, Machine Learning y especialmente Deep Learning, hacen de FastAPI una muy buena opción para APIs web de Data Science / Machine Learning y aplicaciones (entre muchas otras).
    
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  3. docs/es/docs/tutorial/path-params.md

    /// tip | Consejo
    
    Si te estás preguntando, "AlexNet", "ResNet" y "LeNet" son solo nombres de <abbr title="Técnicamente, arquitecturas de modelos de Deep Learning">modelos</abbr> de Machine Learning.
    
    ///
    
    ### Declarar un *path parameter* { #declare-a-path-parameter }
    
    Luego crea un *path parameter* con una anotación de tipo usando la clase enum que creaste (`ModelName`):
    
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  4. docs/en/docs/_llm-test.md

    * <abbr title="A group of machines that are configured to be connected and work together in some way.">cluster</abbr>
    * <abbr title="A method of machine learning that uses artificial neural networks with numerous hidden layers between input and output layers, thereby developing a comprehensive internal structure">Deep Learning</abbr>
    
    ### The abbr gives a full phrase and an explanation { #the-abbr-gives-a-full-phrase-and-an-explanation }
    
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  5. docs/uk/docs/tutorial/path-params.md

    ///
    
    /// tip | Порада
    
    Якщо вам цікаво, "AlexNet", "ResNet" та "LeNet" — це просто назви ML моделей <abbr title="Технічно, архітектури Deep Learning моделей">Machine Learning</abbr>.
    
    ///
    
    
    ### Оголосіть *параметр шляху*
    
    Потім створіть *параметр шляху* з анотацією типу, використовуючи створений вами клас enum (`ModelName`):
    
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  6. docs/en/docs/async.md

    * **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.
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  7. docs/de/docs/tutorial/path-params.md

    {* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}
    
    
    /// tip | Tipp
    
    Falls Sie sich fragen, was „AlexNet“, „ResNet“ und „LeNet“ ist, das sind Namen von <abbr title="Genau genommen, Deep-Learning-Modellarchitekturen">Modellen</abbr> für maschinelles Lernen.
    
    ///
    
    ### Einen *Pfad-Parameter* deklarieren { #declare-a-path-parameter }
    
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  8. docs/fr/docs/async.md

    * L'apprentissage automatique (ou **Machine Learning**) : cela nécessite de nombreuses multiplications de matrices et vecteurs. Imaginez une énorme feuille de calcul remplie de nombres que vous multiplierez entre eux tous au même moment.
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  9. docs/es/docs/_llm-test.md

    * <abbr title="Un método de machine learning que usa redes neuronales artificiales con numerosas capas ocultas entre las capas de entrada y salida, desarrollando así una estructura interna completa">Deep Learning</abbr>
    
    ### El abbr da una frase completa y una explicación { #the-abbr-gives-a-full-phrase-and-an-explanation }
    
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  10. docs/pt/docs/_llm-test.md

    Algum texto
    ///
    
    /// check | Verifique
    Algum texto
    ///
    
    /// tip | Dica
    Algum texto
    ///
    
    /// warning | Atenção
    Algum texto
    ///
    
    /// danger | Cuidado
    Algum texto
    ///
    
    ////
    
    //// tab | Informações
    
    Abas e blocos `Info`/`Note`/`Warning`/etc. devem ter a tradução do seu título adicionada após uma barra vertical (`|`).
    
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