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 |
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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 (Print) | 9781450361866 |
DOIs | |
Publication status | Published - 30 Mar 2019 |
Keywords
- feature cnn
- machine learning
- scaling
- mnist
- resnet
- deep learning