This program utilizes neural networks to recognize handwritten digits from images. It employs a convolutional neural network (CNN) architecture that effectively learns patterns and features in digit images. Key functionalities include:

Data Preprocessing: Normalizes and augments the MNIST dataset to enhance model performance. Model Architecture: Features a multi-layered CNN with convolutional, pooling, and fully connected layers, optimized for accuracy. Training and Validation: Implements techniques such as dropout and batch normalization to prevent overfitting and improve validation results. Performance Evaluation: Achieves high accuracy on the test dataset, demonstrating effective recognition of handwritten digits.