https://github.com/hafs96/cnn-based_handwritten_digit_recognition

This repository contains a project focused on handwritten digit classification using a Convolutional Neural Network (CNN). The goal was to classify digits (0-9) from the widely-used MNIST dataset.
https://github.com/hafs96/cnn-based_handwritten_digit_recognition

covolution-neural-network datasets keras machine-learning model neural-networks pandas pooling-max-average python tensorflow

Last synced: 5 months ago
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This repository contains a project focused on handwritten digit classification using a Convolutional Neural Network (CNN). The goal was to classify digits (0-9) from the widely-used MNIST dataset.

• Host: GitHub
• URL: https://github.com/hafs96/cnn-based_handwritten_digit_recognition
• Owner: hafs96
• Created: 2025-01-22T14:15:29.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-22T14:37:23.000Z (over 1 year ago)
• Last Synced: 2025-01-22T15:27:19.418Z (over 1 year ago)
• Topics: covolution-neural-network, datasets, keras, machine-learning, model, neural-networks, pandas, pooling-max-average, python, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 1.93 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
Handwritten Digit Classification Using CNNs




This project demonstrates the use of a Convolutional Neural Network (CNN) 

to classify handwritten digits (0-9). The model was trained and evaluated on the 

popular MNIST dataset, achieving high accuracy. This project highlights my first 

steps in neural networks and machine learning.



Project Highlights



  
Implemented a CNN with TensorFlow and Keras frameworks.

  
Visualized training and validation accuracy and loss over epochs.

  
Evaluated the model on test data and displayed predictions alongside actual labels.



Key Features



  
Python 3.10

  
Framework: TensorFlow 2.12.0

  
Key Libraries: NumPy, Matplotlib



Technical Configuration



  
Processor: Intel i5-1035G1

  
RAM: 8 GB

  
GPU: Not used (training on CPU)



Repository



You can explore the project details, code, and results in this repository: 



GitHub Repository

.