Handwritten Digit Recognition Using Neural Networks

This project demonstrates the development of a neural network model for recognizing handwritten digits with high precision using the MNIST database. The model's design focuses on preprocessing the image data, constructing and optimizing a neural network architecture, and training the model to classify digits from 0 to 9 effectively. The final evaluation of the model showcases its accuracy and ability to generalize on unseen data, highlighting essential skills in machine learning and neural network design.