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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 .