Abstract
Deep learning (DL) has gained a lot of popularity in the science and business community. It has been successful in a range of applications, especially in computer vision. This paper presents results from applying scaled MNIST images dataset to a popular implementation of deep learning called ResNet. This is a valuable contribution because in general convolutional networks are not scale invariant. Our objective is to explore the behavior of a residual neural network when trained and evaluated using three different datasets of scaled MNIST images.
Original language | English |
---|---|
Title of host publication | ICBDE'19: Proceedings of the 2019 International Conference on Big Data and Education |
Place of Publication | New York |
Publisher | Association for Computing Machinery (ACM) |
Pages | 77-81 |
Number of pages | 5 |
ISBN (Electronic) | 9781450361866 |
ISBN (Print) | 9781450361866 |
DOIs | |
Publication status | Published - 30 Mar 2019 |
Event | 2019 International Conference on Big Data and Education - University of Greenwich, London, United Kingdom Duration: 30 Mar 2019 → 1 Apr 2019 https://www.icbde.org/2019.html (Link to conference website) |
Publication series
Name | ACM International Conference Proceeding Series |
---|
Conference
Conference | 2019 International Conference on Big Data and Education |
---|---|
Abbreviated title | ICBDE 2019 |
Country/Territory | United Kingdom |
City | London |
Period | 30/03/19 → 1/04/19 |
Internet address |
|
Keywords
- feature cnn
- machine learning
- scaling
- mnist
- resnet
- deep learning
- Deep learning
- Feature cnn
- Machine learning
- Mnist
- Scaling
- Resnet
ASJC Scopus subject areas
- Software
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Networks and Communications