Abstract
The world, as we know, it would not exist without the development of machine learning (ML). Its application in processing information gives the human more advantages than in any other age. The use of ML brings the opportunity to perform categorizations into a big amount of elements and into areas that the human simply can not do it without this technology. But the development of ML algorithms is complex since it requires specific theoretical bases and the development of skills to achieve the tuning of these algorithms. Given the wide variety of algorithms and techniques to perform element classification, this chapter focuses on the study of Convolutional Neural Networks (CNN), and the development of tests that facilitate the understanding of the effects of their basic parameters. The classification objects used are images, and only the multiple class classification model is considered.
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Ramos-Michel, A., Pérez-Cisneros, M., Cuevas, E., Zaldivar, D. (2021). Image Classification with Convolutional Neural Networks. In: Oliva, D., Houssein, E.H., Hinojosa, S. (eds) Metaheuristics in Machine Learning: Theory and Applications. Studies in Computational Intelligence, vol 967. Springer, Cham. https://doi.org/10.1007/978-3-030-70542-8_18
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