Sudan Jha1, Sultan Ahmad This email address is being protected from spambots. You need JavaScript enabled to view it.2, Hikmat A. M. Abdeljaber2, A. A. Hamad3, and Malik Bader Alazzam4

1School of Sciences, Christ (Deemed to be University), NCR-New Delhi Campus, India
2Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia.
3College of Science, Tikrit University, Iraq
4Faculty of Computer Science and Informatics, Amman Arab University, Jordan


 

Received: June 27, 2021
Accepted: July 29, 2021
Publication Date: September 11, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202204_25(2).0014  


ABSTRACT


Deep learning has paved the way for critical and revolutionary applications in almost every field of life in general. Ranging from engineering to healthcare, machine learning and deep learning has left its mark as the state-of-the-art technology application which holds the epitome of a reasonable high benchmarked solution. Incorporating neural network architectures into applications has become a common part of any software development process. In this paper, we perform a comparative analysis on the different transfer learning approaches in the domain of hand-written digit recognition. We use two performance measures, loss and accuracy. We later visualize the different results for the training and validation datasets and reach to a unison conclusion. This paper aims to target the drawbacks of the electronic whiteboard with simultaneous focus on the suitable model selection procedure for the digit recognition problem.


Keywords: Learning; Transfer Learning; Deep Learning; Machine Learning; Electronic Whiteboard


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