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Deep Learning Applications

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Hardback
156 Pages
12 May 2026
RRP: $263.00
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A comprehensive compilation that explores a wide range of applications where deep learning techniques have had impact and its practical uses. The book covers fields like healthcare, finance, autonomous systems, and more. The methodologies such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and reinforcement learning, have been included.

In healthcare deep learning is used for medical image analysis, disease prediction, and personalized medicine. In the financial sector, aspects like fraud detection, algorithmic trading, and risk management are reviewed. Also autonomous systems, including self-driving cars and drones are explored.

Each chapter includes detailed case studies, experimental results, and insights from leading experts, offering readers both foundational knowledge and advanced techniques. The book serves as a valuable resource for researchers, practitioners, and students seeking to understand the breadth and depth of deep learning applications.

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    Product Details
    ISBN-13
    9781041105725
    Author
    Laith Abualigah
    Publisher
    CRC Press
    Published
    12 May 2026
    Format
    Hardback
    Pages
    156

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    RRP: $263.00
    $260.00
    Ships in 2-5 business days

    Delivery estimate

    Hurry up! Current stock:

    Deep Learning Applications

    RRP: $263.00
    $260.00

    Description

    A comprehensive compilation that explores a wide range of applications where deep learning techniques have had impact and its practical uses. The book covers fields like healthcare, finance, autonomous systems, and more. The methodologies such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and reinforcement learning, have been included.

    In healthcare deep learning is used for medical image analysis, disease prediction, and personalized medicine. In the financial sector, aspects like fraud detection, algorithmic trading, and risk management are reviewed. Also autonomous systems, including self-driving cars and drones are explored.

    Each chapter includes detailed case studies, experimental results, and insights from leading experts, offering readers both foundational knowledge and advanced techniques. The book serves as a valuable resource for researchers, practitioners, and students seeking to understand the breadth and depth of deep learning applications.

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