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 .