https://github.com/a9na/handwritten-digit-recognition-cnn

This project involves creating a Convolutional Neural Network (CNN) to classify handwritten digits (0-9) using the MNIST dataset. The MNIST dataset consists of 28x28 grayscale images of handwritten digits. The goal is to build, train, and evaluate a CNN model to accurately predict the digit in each image.
https://github.com/a9na/handwritten-digit-recognition-cnn

Last synced: 2 months ago
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This project involves creating a Convolutional Neural Network (CNN) to classify handwritten digits (0-9) using the MNIST dataset. The MNIST dataset consists of 28x28 grayscale images of handwritten digits. The goal is to build, train, and evaluate a CNN model to accurately predict the digit in each image.

• Host: GitHub
• URL: https://github.com/a9na/handwritten-digit-recognition-cnn
• Owner: a9na
• Created: 2024-07-31T12:37:25.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-07-31T12:39:10.000Z (9 months ago)
• Last Synced: 2025-01-09T03:38:39.279Z (4 months ago)
• Language: Python
• Size: 4.88 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# handwritten-digit-recognition-cnn

This project involves creating a Convolutional Neural Network (CNN) to classify handwritten digits (0-9) using the MNIST dataset. The MNIST dataset consists of 28x28 grayscale images of handwritten digits. The goal is to build, train, and evaluate a CNN model to accurately predict the digit in each image.