A Comparison of the State-of-the-Art Deep Learning Platforms: An Experimental Study

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dc.contributor.authorKabakuş, Abdullah Talha
dc.date.accessioned2025-10-11T20:42:17Z
dc.date.available2025-10-11T20:42:17Z
dc.date.issued2020
dc.departmentDüzce Üniversitesien_US
dc.description.abstractDeep learning, a subfield of machine learning, has proved its efficacy on a wide range of applications including but not limited to computer vision, text analysis and natural language processing, algorithm enhancement, computational biology, physical sciences, and medical diagnostics by producing results superior to the state-of-the-art approaches. When it comes to the implementation of deep neural networks, there exist various state-of-the-art platforms. Starting from this point of view, a qualitative and quantitative comparison of the state-of-the-art deep learning platforms is proposed in this study in order to shed light on which platform should be utilized for the implementations of deep neural networks. Two state-of-the-art deep learning platforms, namely, (i) Keras, and (ii) PyTorch were included in the comparison within this study. The deep learning platforms were quantitatively examined through the models based on three most popular deep neural networks, namely, (i) Feedforward Neural Network (FNN), (ii) Convolutional Neural Network (CNN), and (iii) Recurrent Neural Network (RNN). The models were evaluated on three evaluation metrics, namely, (i) training time, (ii) testing time, and (iii) prediction accuracy. According to the experimental results, while Keras provided the best performance for both FNNs and CNNs, PyTorch provided the best performance for RNNs expect for one evaluation metric, which was the testing time. This experimental study should help deep learning engineers and researchers to choose the most suitable platform for the implementations of their deep neural networks.en_US
dc.identifier.doi10.35377/saucis.03.03.776573
dc.identifier.endpage182en_US
dc.identifier.issn2636-8129
dc.identifier.issue3en_US
dc.identifier.startpage169en_US
dc.identifier.urihttps://doi.org/10.35377/saucis.03.03.776573
dc.identifier.urihttps://hdl.handle.net/20.500.12684/20916
dc.identifier.volume3en_US
dc.institutionauthorKabakuş, Abdullah Talha
dc.language.isoenen_US
dc.publisherSakarya Universityen_US
dc.relation.ispartofSakarya University Journal of Computer and Information Sciencesen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_DergiPark_20250911
dc.subjectArtificial Intelligenceen_US
dc.subjectYapay Zekaen_US
dc.titleA Comparison of the State-of-the-Art Deep Learning Platforms: An Experimental Studyen_US
dc.typeResearch Articleen_US

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