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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
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    References listed on IDEAS

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    1. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
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