Recognition of handwritten digital neural network construction and improvement
- 1 Faculty of Engineering, University of Bristol, Bristol, England, United Kingdom BS8 1TH
* Author to whom correspondence should be addressed.
Abstract
More and more neural network algorithms are being invented and improved, but the most basic algorithms are still useful in learning or practice areas. This essay is about building a digital recognition neural network from scratch, whose implementation has a completely original core code and details and precisely elaborates the whole process of basic neural network construction through detailed mathematical derivation, combined with the idea of programming. Furthermore, this project deeply researched and analyzed recognition accuracy according to the identified data of the neural network built for the project, then improved the algorithm used by increasing the accuracy from 86.93% to 99.1% and finally successfully explained the role of raw data preprocessing.
Keywords
Neural Networks, Recognizing Handwritten Digits, Backpropagation, One-hot Encoding, Python Implementing Neural Networks.
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Cite this article
Shao,Z. (2023). Recognition of handwritten digital neural network construction and improvement. Applied and Computational Engineering,6,575-581.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
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