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    Information Theory for Data Science

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    Author(s)
    Suh, Changho
    Language
    English
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    Abstract
    Information theory deals with mathematical laws that govern the flow, representation and transmission of information. The most significant achievement of the field is the invention of digital communication which forms the basis of our daily-life digital products such as smart phones, laptops and any IoT devices. Recently it has also found important roles in a spotlight field that has been revolutionized during the past decades: data science. This book aims at demonstrating modern roles of information theory in a widening array of data science applications. The first and second parts of the book covers the core concepts of information theory: basic concepts on several key notions; and celebrated source and channel coding theorems which concern the fundamental limits of communication. The last part focuses on applications that arise in data science, including social networks, ranking, and machine learning. The book is written as a text for senior undergraduate and graduate students working on Information Theory and Communications, and it should also prove to be a valuable reference for professionals and engineers from these fields.
    URI
    https://library.oapen.org/handle/20.500.12657/87165
    Keywords
    Information Theory, Data Science, Source Coding, Channel Coding, DNA sequencing, DNA sequencing, Top-K ranking, Supervised learning, Unsupervised Learning, Generative Adversarial Networks (GANs), TensorFlow
    DOI
    10.1561/9781638281153
    ISBN
    9781638281146, 9781638281153
    Publisher
    Now Publishers
    Publication date and place
    2023
    Series
    NowOpen,
    Classification
    Information technology: general topics
    Pages
    417
    Rights
    https://creativecommons.org/licenses/by-nc/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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