Demystifying Deep Learning Building Blocks

Humberto de Jesús Ochoa Domínguez (), Vianey Guadalupe Cruz Sánchez and Osslan Osiris Vergara Villegas
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Humberto de Jesús Ochoa Domínguez: Electrical and Computer Engineering Department, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
Vianey Guadalupe Cruz Sánchez: Electrical and Computer Engineering Department, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
Osslan Osiris Vergara Villegas: Industrial and Manufacturing Engineering Department, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico

Mathematics, 2024, vol. 12, issue 2, 1-26

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.

Keywords: deep learning; artificial neural networks; convolutional neural layer; activation layer; pooling layer; forward propagation; backpropagation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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