Thesis Details
Evoluční návrh konvolučních neuronových sítí
The aim of this Master's thesis is to describe basic technics of evolutionary computing, convolutional neural networks (CNN), and automated design of neural networks using neuroevolution (NAS - Neural Architecture Search). NAS techniques are currently being researched more and more, as they speed up and simplify the lengthy and complicated process of designing artificial neural networks. These techniques are also able to search for unconventional architectures that would not be found by classic methods. The work also contains the design and implementation of software capable of automated development of convolutional neural networks using the open-source library TensorFlow. The program uses a multiobjective NSGA-II algorithm for designing accurate and compact CNNs.
evolutionary algorithms, convolutional neural networks, neuroevolution, TensorFlow
Drábek Vladimír, doc. Ing., CSc. (DCSY FIT BUT), člen
Fučík Otto, doc. Dr. Ing. (DCSY FIT BUT), člen
Křivka Zbyněk, Ing., Ph.D. (DIFS FIT BUT), člen
Martínek Tomáš, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Smrčka Aleš, Ing., Ph.D. (DITS FIT BUT), člen
@mastersthesis{FITMT23301,
author = "J\'{a}n Prista\v{s}",
type = "Master's thesis",
title = "Evolu\v{c}n\'{i} n\'{a}vrh konvolu\v{c}n\'{i}ch neuronov\'{y}ch s\'{i}t\'{i}",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2021,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/23301/"
}