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Autor(en): 
  • Thushan Ganegedara
  • TensorFlow 2.0 in Action 
     

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


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  September 2022  
    Genre:  EDV / Informatik 
    ISBN:  9781617298349 
    EAN-Code: 
    9781617298349 
    Verlag:  Pearson Academic 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 234 mm / B 190 mm / D 40 mm 
    Gewicht:  1160 gr 
    Seiten:  325 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Unlock the TensorFlow design secrets behind successful deep learning applications! Deep learning StackOverflow contributor Thushan Ganegedara teaches you the new features of TensorFlow 2 in this hands-on guide.

    In TensorFlow in Action you will learn:

        Fundamentals of TensorFlow
        Implementing deep learning networks
        Picking a high-level Keras API for model building with confidence
        Writing comprehensive end-to-end data pipelines
        Building models for computer vision and natural language processing
        Utilizing pretrained NLP models
        Recent algorithms including transformers, attention models, and ElMo

    In TensorFlow in Action, you'll dig into the newest version of Google's amazing TensorFlow framework as you learn to create incredible deep learning applications. Author Thushan Ganegedara uses quirky stories, practical examples, and behind-the-scenes explanations to demystify concepts otherwise trapped in dense academic papers. As you dive into modern deep learning techniques like transformer and attention models, you’ll benefit from the unique insights of a top StackOverflow contributor for deep learning and NLP.

    Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

    About the technology
    Google’s TensorFlow framework sits at the heart of modern deep learning. Boasting practical features like multi-GPU support, network data visualization, and easy production pipelines using TensorFlow Extended (TFX), TensorFlow provides the most efficient path to professional AI applications. And the Keras library, fully integrated into TensorFlow 2, makes it a snap to build and train even complex models for vision, language, and more.

    About the book
    TensorFlow in Action teaches you to construct, train, and deploy deep learning models using TensorFlow 2. In this practical tutorial, you’ll build reusable skill hands-on as you create production-ready applications such as a French-to-English translator and a neural network that can write fiction. You’ll appreciate the in-depth explanations that go from DL basics to advanced applications in NLP, image processing, and MLOps, complete with important details that you’ll return to reference over and over.

    What's inside

        Covers TensorFlow 2.9
        Recent algorithms including transformers, attention models, and ElMo
        Build on pretrained models
        Writing end-to-end data pipelines with TFX

    About the reader
    For Python programmers with basic deep learning skills.

    About the author
    Thushan Ganegedara is a senior ML engineer at Canva and TensorFlow expert. He holds a PhD in machine learning from the University of Sydney.

    Table of Contents
    PART 1 FOUNDATIONS OF TENSORFLOW 2 AND DEEP LEARNING
    1 The amazing world of TensorFlow
    2 TensorFlow 2
    3 Keras and data retrieval in TensorFlow 2
    4 Dipping toes in deep learning
    5 State-of-the-art in deep learning: Transformers
    PART 2 LOOK MA, NO HANDS! DEEP NETWORKS IN THE REAL WORLD
    6 Teaching machines to see: Image classification with CNNs
    7 Teaching machines to see better: Improving CNNs and making them confess
    8 Telling things apart: Image segmentation
    9 Natural language processing with TensorFlow: Sentiment analysis
    10 Natural language processing with TensorFlow: Language modeling
    PART 3 ADVANCED DEEP NETWORKS FOR COMPLEX PROBLEMS
    11 Sequence-to-sequence learning: Part 1
    12 Sequence-to-sequence learning: Part 2
    13 Transformers
    14 TensorBoard: Big brother of TensorFlow
    15 TFX: MLOps and deploying models with TensorFlow

      



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