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    The Shallow and the Deep

    A biased introduction to neural networks and old school machine learning

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    Author(s)
    Biehl, Michael
    Language
    English
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    Abstract
    The Shallow and the Deep is a collection of lecture notes that offers an accessible introduction to neural networks and machine learning in general. However, it was clear from the beginning that these notes would not be able to cover this rapidly changing and growing field in its entirety. The focus lies on classical machine learning techniques, with a bias towards classification and regression. Other learning paradigms and many recent developments in, for instance, Deep Learning are not addressed or only briefly touched upon. Biehl argues that having a solid knowledge of the foundations of the field is essential, especially for anyone who wants to explore the world of machine learning with an ambition that goes beyond the application of some software package to some data set. Therefore, The Shallow and the Deep places emphasis on fundamental concepts and theoretical background. This also involves delving into the history and pre-history of neural networks, where the foundations for most of the recent developments were laid. These notes aim to demystify machine learning and neural networks without losing the appreciation for their impressive power and versatility.
    URI
    https://library.oapen.org/handle/20.500.12657/93936
    Keywords
    Neural networks; Machine learning; Computational Intelligence; Computer Science
    DOI
    10.21827/648c59c1a467e
    ISBN
    9789403430287, 9789403430270
    Publisher
    University of Groningen Press
    Publisher website
    https://books.ugp.rug.nl/index.php/ugp
    Publication date and place
    Groningen, 2023
    Classification
    Neural networks and fuzzy systems
    Machine learning
    Computer science
    Pages
    294
    Rights
    https://creativecommons.org/licenses/by-nc-sa/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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