A post COVID Machine Learning approach in Teaching and Learning methodology to alleviate drawbacks of the e-whiteboards
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 their mark as the state-of-the-art technology application which holds the epitome of a reasonable...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Tamkang University Press
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/a829e86d999f48d097f42f907c18c507 |
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| Sumario: | 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 their 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 a unison conclusion. This paper aims to target the drawbacks of the electronic whiteboard with a simultaneous focus on the suitable model selection procedure for the digit recognition problem. |
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