Logo Oapen
  • Join
    • Deposit
    • For Librarians
    • For Publishers
    • For Researchers
    • Funders
    • Resources
    • OAPEN
        View Item 
        •   OAPEN Home
        • View Item
        •   OAPEN Home
        • View Item
        JavaScript is disabled for your browser. Some features of this site may not work without it.

        The Shallow and the Deep

        A biased introduction to neural networks and old school machine learning

        Thumbnail
        Download PDF Viewer
        Web Shop
        Author(s)
        Biehl, Michael
        Language
        English
        Show full item record
        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
        9789403430270, 9789403430287
        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

        Browse

        All of OAPENSubjectsPublishersLanguagesCollections

        My Account

        LoginRegister

        Export

        Repository metadata
        Logo Oapen
        • For Librarians
        • For Publishers
        • For Researchers
        • Funders
        • Resources
        • OAPEN

        Newsletter

        • Subscribe to our newsletter
        • view our news archive

        Follow us on

        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.

        OAPEN is based in the Netherlands, with its registered office in the National Library in The Hague.

        Director: Niels Stern

        Address:
        OAPEN Foundation
        Prins Willem-Alexanderhof 5
        2595 BE The Hague
        Postal address:
        OAPEN Foundation
        P.O. Box 90407
        2509 LK The Hague

        Websites:
        OAPEN Home: www.oapen.org
        OAPEN Library: library.oapen.org
        DOAB: www.doabooks.org

         

         

        Export search results

        The export option will allow you to export the current search results of the entered query to a file. Differen formats are available for download. To export the items, click on the button corresponding with the preferred download format.

        A logged-in user can export up to 15000 items. If you're not logged in, you can export no more than 500 items.

        To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

        After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.