Instructions:
• Use the numpad to select MNIST digits and see real CNN processing
• Click "Draw Your Own" to draw custom digits for classification
• Watch feature maps update as data flows through each layer
• Start at epoch 0 (random weights) and step through training
• Click "Train Epoch" to advance one training epoch at a time
• Observe how feature maps evolve as the network learns

CNN Architecture:
• Input Layer: 28×28 grayscale digit images
• Conv Layer 1: 4 filters (3×3) → ReLU activation
• Max Pool 1: 2×2 pooling → reduces spatial size
• Conv Layer 2: 8 filters (3×3) → ReLU activation
• Max Pool 2: 2×2 pooling → further size reduction
• Flatten: Convert 2D feature maps to 1D vector
• Dense Layer: 128 neurons → ReLU activation
• Output Layer: 10 neurons → Softmax (digit probabilities)

Training Process:
• Real Model States: Each epoch uses actual trained weights
• Loss Function: Cross-entropy loss for multi-class classification
• Optimizer: Adam optimizer with learning rate 0.001
• Performance: Watch accuracy improve from 10% to 95%+