This project utilizes an Artificial Neural Network (ANN) to classify handwritten digits (0-9) from the MNIST dataset. The ANN architecture includes layers with ReLU activation functions and a Softmax output layer for multi-class classification. It achieves an accuracy of 97.5% on the test dataset.

The neural network architecture consists of:

• Input layer: 784 nodes (corresponding to 28x28 pixel images flattened)
• Hidden layers: Multiple layers with ReLU activation
• Output layer: 10 nodes with Softmax activation (one for each digit class)

The model is trained using:

• Loss function: Sparse categorical cross-entropy
• Optimizer: Adam optimizer with default parameters
• Metrics: Accuracy

During training, graphs are generated to visualize:

• Training and validation loss over epochs
• Training and validation accuracy over epochs