This paper is published in Volume-6, Issue-3, 2020
Area
Machine Learning
Author
Shivam Srivastava, Richa Sharma, Pratyaksha Jindal, Sikandar Singh Sandhu, Pratyush, Atul Kumar
Org/Univ
Babu Banarasi Das National Institute of Technology and Management, Lucknow, Uttar Pradesh, India
Pub. Date
26 May, 2020
Paper ID
V6I3-1301
Publisher
Keywords
Handwritten digit classifier, MNIST, Deep learning, Convolutional neural networks, Machine learning, PyTorch, Neural networks

Citationsacebook

IEEE
Shivam Srivastava, Richa Sharma, Pratyaksha Jindal, Sikandar Singh Sandhu, Pratyush, Atul Kumar. Handwritten digit classification using Convolutional Neural Networks, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Shivam Srivastava, Richa Sharma, Pratyaksha Jindal, Sikandar Singh Sandhu, Pratyush, Atul Kumar (2020). Handwritten digit classification using Convolutional Neural Networks. International Journal of Advance Research, Ideas and Innovations in Technology, 6(3) www.IJARIIT.com.

MLA
Shivam Srivastava, Richa Sharma, Pratyaksha Jindal, Sikandar Singh Sandhu, Pratyush, Atul Kumar. "Handwritten digit classification using Convolutional Neural Networks." International Journal of Advance Research, Ideas and Innovations in Technology 6.3 (2020). www.IJARIIT.com.

Abstract

This research study throws light on one of the most common use-cases of Handwritten Digit recognition which can be seen being implemented by using a particular Deep Learning technique for pattern recognition known as Convolutional Neural Networks which works similarly to the functionality of neurons in a human brain. We have trained and tested the MNIST dataset using this technique and implemented a classifier to predict the pattern of the digits.
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