Key Features:
Dual CNN Architectures: Basic ImprovedNN and enhanced EnhancedCNN models
Comprehensive Training Pipeline: Complete data preprocessing, training loops, and validation
Performance Optimization: Learning rate scheduling, dropout regularization, and GPU acceleration
Visualization Tools: Training metrics plots and sample image display functions
Model Persistence: Save and load trained model weights for future use
Results:
Achieves >99% accuracy on MNIST test set
Implements modern deep learning best practices
Includes detailed documentation and code explanations