Year: 2022
Type: article
Abstract: The idea of artificial intelligence has been in our lives since the second quarter of the 20th century. Up to now, it has enjoyed great popularity in some cases, and in others it has been forgotten. In the 2000s, the development of computer hardware and developments in artificial neural networks led to an extensive use of artificial intelligence algorithms. Deep learning studies are the pioneers of artificial intelligence studies. Deep learning is not only used in computer science and object detection, but also in many disciplines and many tasks. Different architectures, hardware and software frameworks have been developed for deep learning studies. The fact that there are so many deep learning tools and architectures can confuse the work of researchers who want to use deep learning tools to solve their problems. In this study we want to create an understanding of the concept of deep learning. We present the concept of deep learning and its subcomponents systematically. We then introduce the common deep learning frameworks that are widely used recently. A review was created from the studies in which these frameworks were examined according to different criteria such as performance, time, accuracy, searched on Google, followed by GitHub. In this study, we would like to be a guide for researchers and readers who are unfamiliar with deep learning algorithms but want to use deep learning tools in their studies.
Source: DergiPark (Istanbul University)
Authors: Aktürk, Süleyman, Serbest, Kasım
Institution: Sakarya Uygulamalı Bilimler Üniversitesi
Language: Turkish
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4312197180