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Autor(en): 
  • Sebastian Raschka
  • Machine Learning Q and AI: 30 Essential Questions and Answers on Machine Learning and AI 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  April 2024  
    Genre:  EDV / Informatik 
     
    Basic statistics / Behavioral Economics / Biostatistics / computer books / Computer Programming / Software Development / Computer programming / software engineering / COMPUTERS / Data Science / Machine Learning / COMPUTERS / Programming / General
    ISBN:  9781718503762 
    EAN-Code: 
    9781718503762 
    Verlag:  Random House N.Y. 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 234 mm / B 177 mm / D 18 mm 
    Gewicht:  514 gr 
    Seiten:  264 
    Zus. Info:  CRE GLD BLK 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Learn the answers to 30 cutting-edge questions in machine learning and AI and level up your expertise in the field.

    If you’re ready to venture beyond introductory concepts and dig deeper into machine learning, deep learning, and AI, the question-and-answer format of Machine Learning Q and AI will make things fast and easy for you, without a lot of mucking about.

    Born out of questions often fielded by author Sebastian Raschka, the direct, no-nonsense approach of this book makes advanced topics more accessible and genuinely engaging. Each brief, self-contained chapter journeys through a fundamental question in AI, unraveling it with clear explanations, diagrams, and hands-on exercises.

    WHAT'S INSIDE:

    FOCUSED CHAPTERS: Key questions in AI are answered concisely, and complex ideas are broken down into easily digestible parts.

    WIDE RANGE OF TOPICS: Raschka covers topics ranging from neural network architectures and model evaluation to computer vision and natural language processing.

    PRACTICAL APPLICATIONS: Learn techniques for enhancing model performance, fine-tuning large models, and more.

    You’ll also explore how to:
    • Manage the various sources of randomness in neural network training
    • Differentiate between encoder and decoder architectures in large language models
    • Reduce overfitting through data and model modifications
    • Construct confidence intervals for classifiers and optimize models with limited labeled data
    • Choose between different multi-GPU training paradigms and different types of generative AI models
    • Understand performance metrics for natural language processing
    • Make sense of the inductive biases in vision transformers

    If you’ve been on the hunt for the perfect resource to elevate your understanding of machine learning, Machine Learning Q and AI will make it easy for you to painlessly advance your knowledge beyond the basics.

      



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