Article
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Demystifying the Deep Learning Building Blocks
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Version 1
: Received: 5 December 2023 / Approved: 6 December 2023 / Online: 6 December 2023 (07:27:12 CET)
A peer-reviewed article of this Preprint also exists.
Ochoa Domínguez, H.J.; Cruz Sánchez, V.G.; Vergara Villegas, O.O. Demystifying Deep Learning Building Blocks. Mathematics 2024, 12, 296. Ochoa Domínguez, H.J.; Cruz Sánchez, V.G.; Vergara Villegas, O.O. Demystifying Deep Learning Building Blocks. Mathematics 2024, 12, 296.
Abstract
Building deep learning models proposed by third parties can become a simple task when specialized libraries are used. However, a great deal of 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 as well as their dimensions. This information is limited and broken up in different literature. In this article, we collect and explain in-depth the building blocks to design deep learning models, 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.
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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