Instructions:
• Select different filter types to see various convolution effects
• Adjust filter values manually using the custom filter editor
• Choose different input images (simple pattern, vertical/horizontal edges, checkerboard, gradient)
• Experiment with pooling operations (max pooling, average pooling)
• Change pooling window size (2×2, 3×3, 4×4) to see different downsampling effects
• Hover over cells in convolution or pooling outputs to highlight corresponding input regions

Convolution Fundamentals:
• Filters (Kernels): 3×3 matrices that detect specific features (edges, blur, sharpen)
• Feature Maps: Convolution output showing where features were detected
• Interactive Highlighting: Hover over outputs to see which input pixels contributed

Pooling Operations:
• Max Pooling: Takes maximum value in each pooling window
• Average Pooling: Takes average value in each pooling window
• Purpose: Reduces spatial dimensions while preserving important features
• Window Size: Controls how much downsampling occurs