Mihai Cristian Chirodea
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Mihai Cristian Chirodea has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Cornelia Emilia Gordan
Department of Electronics and Telecommunications, Electrical Engineering and Information Technology Faculty, University of Oradea, 410087 Oradea, Romania
1 shared publication - Cornelia Mihaela Novac
Department of Electrical Engineering, Electrical Engineering and Information Technology Faculty, University of Oradea, 410087 Oradea, Romania
1 shared publication - Mihai Oproescu
Faculty of Electronics, Communication and Computers, Pitesti University Centre, National University of Science and Technology Politehnica Bucharest, 110040 Pitesti, Romania
1 shared publication - Nicu Bizon
Department of Electronics, Computers and Electrical Engineering, Pitești University Centre, National University of Science and Technology POLITEHNICA Bucharest, 110040 Pitesti, Romania
1 shared publication - Ovidiu Constantin Novac
Department of Computers and Information Technology, Faculty of Electrical Engineering and Information Technology, University of Oradea, 410087 Oradea, Romania
1 shared publication - Ovidiu Petru Stan
Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
1 shared publication
Latest Publications
- Open Access
Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network
In this paper, we present an analysis of important aspects that arise during the development of neural network applications. Our aim is to determine if the choice of library can impact the system’s overall performance, either during training or design, and to extract a set of criteria that could be used to highlight the advantages and disadvantages of each library under consideration. To do so, we first extracted the previously mentioned aspects by comparing two of the most popular neural network libraries—PyTorch and TensorFlow—and then we performed an analysis on the obtained results, with the intent of determining if our initial hypothesis was correct. In the end, the results of the analysis are gathered, and an overall picture of what tasks are better suited for what library is presented.
Open AccessAnalysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network
In this paper, we present an analysis of important aspects that arise during the development of neural network applications. Our aim is to determine if the choice of library can impact the system’s overall performance, either during training or design, and to extract a set of criteria that could be used to highlight the advantages and disadvantages of each library under consideration. To do so, we first extracted the previously mentioned aspects by comparing two of the most popular neural network libraries—PyTorch and TensorFlow—and then we performed an analysis on the obtained results, with the intent of determining if our initial hypothesis was correct. In the end, the results of the analysis are gathered, and an overall picture of what tasks are better suited for what library is presented.