Implementing Convolutional Neural Networks for Simple Image Classification
Aditeya Nanda1, Praveen Kumar2, Seema Rawat3
1Aditeya Nanda, Department of Computer Science & Engineering, Amity University, (Uttar Pradesh), Noida, India.
2Praveen Kumar*, Department of Computer Science & Engineering, Amity University, (Uttar Pradesh), Noida, India.
3Seema Rawat, Department of Department of Information Technology, Amity University, (Uttar Pradesh), Noida, India.
Manuscript received on November 16, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 3616-3619 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3279129219/2019©BEIESP | DOI: 10.35940/ijeat.B3279.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In recent years, huge amounts of data in form of images has been efficiently created and accumulated at extraordinary rates. This huge amount of data that has high volume and velocity has presented us with the problem of coming up with practical and effective ways to classify it for analysis. Existing classification systems can never fulfil the demand and the difficulties of accurately classifying such data. In this paper, we built a Convolutional Neural Network (CNN) which is one of the most powerful and popular machine learning tools used in image recognition systems for classifying images from one of the widely used image datasets CIFAR-10. This paper also gives a thorough overview of the working of our CNN architecture with its parameters and difficulties.
Keywords: Convolutional Neural Network, Image recognition, CIFAR-10, Machine learning.