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Debugging intelligence fixing machine de s sinduja (2 resultados)

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  • Idioma: Inglés

    Editorial: Notion Press Media Pvt. Ltd Okt 2025, 2025

    9798901120835

    • Tapa blanda

    Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

    Vendedor de 5 estrellas
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    Condición: Nuevo

    EUR 31,61

    Envío por EUR 35,00 
    Se envía de Alemania a Estados Unidos de America

    Cantidad disponible: 2 disponibles

    Taschenbuch. Condición: Neu. Neuware - The journey of this book has shown that debugging in machine learning is far more than a technical afterthought, it is a discipline in itself. Debugging is not simply the process of fixing broken code but the deeper act of uncovering why machine learning models fail, understanding the roots of those failures, and ensuring systems behave reliably in the environments where they operate. Machine learning models are built upon layers of design decisions, data assumptions, and performance trade-offs. By the time a system reaches production, it carries with it both the strengths and the hidden weaknesses of those choices. Debugging is the practice of interrogating those assumptions, shining light on vulnerabilities, and building confidence that the system will perform as intended in the real world.…

  • Idioma: Inglés

    Editorial: Notion Press Media Pvt. Ltd Okt 2025, 2025

    9798901120842

    • Tapa dura

    Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

    Vendedor de 5 estrellas
    Contactar con el vendedor

    Condición: Nuevo

    EUR 47,18

    Envío por EUR 35,00 
    Se envía de Alemania a Estados Unidos de America

    Cantidad disponible: 2 disponibles

    Buch. Condición: Neu. Neuware - The journey of this book has shown that debugging in machine learning is far more than a technical afterthought, it is a discipline in itself. Debugging is not simply the process of fixing broken code but the deeper act of uncovering why machine learning models fail, understanding the roots of those failures, and ensuring systems behave reliably in the environments where they operate. Machine learning models are built upon layers of design decisions, data assumptions, and performance trade-offs. By the time a system reaches production, it carries with it both the strengths and the hidden weaknesses of those choices. Debugging is the practice of interrogating those assumptions, shining light on vulnerabilities, and building confidence that the system will perform as intended in the real world. …