Hand-Written Digit Classifier

A machine learning project focused on accurate handwritten digit recognition using deep neural networks. The classifier was trained on the MNIST dataset to achieve high accuracy in digit classification.

Technical Details:

• Implemented multi-layer neural network architecture
• Trained on 60,000 training images from MNIST dataset
• Achieved over 95% accuracy on test data
• Used backpropagation and gradient descent optimization