IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i2p296-d1320528.html
   My bibliography  Save this article

Demystifying Deep Learning Building Blocks

Author

Listed:
  • Humberto de Jesús Ochoa Domínguez

    (Electrical and Computer Engineering Department, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
    These authors contributed equally to this work.)

  • Vianey Guadalupe Cruz Sánchez

    (Electrical and Computer Engineering Department, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
    These authors contributed equally to this work.)

  • Osslan Osiris Vergara Villegas

    (Industrial and Manufacturing Engineering Department, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
    These authors contributed equally to this work.)

Abstract

Building deep learning models proposed by third parties can become a simple task when specialized libraries are used. However, much mystery still surrounds the design of new models or the modification of existing ones. These tasks require in-depth knowledge of the different components or building blocks and their dimensions. This information is limited and broken up in different literature. In this article, we collect and explain the building blocks used to design deep learning models in depth, starting from the artificial neuron to the concepts involved in building deep neural networks. Furthermore, the implementation of each building block is exemplified using the Keras library.

Suggested Citation

  • Humberto de Jesús Ochoa Domínguez & Vianey Guadalupe Cruz Sánchez & Osslan Osiris Vergara Villegas, 2024. "Demystifying Deep Learning Building Blocks," Mathematics, MDPI, vol. 12(2), pages 1-26, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:296-:d:1320528
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/2/296/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/2/296/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:296-:d:1320528. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.