Convolution Operation
Core Concepts:
• Local Connectivity: Each output connects to small input region
• Weight Sharing: Same filter applied across entire input
• Translation Equivariance: Shifted input produces shifted output
• Hierarchical Features: Build complex from simple patterns
Key Parameters
🔍 Number of Filters
Controls depth of output feature maps and model capacity
📐 Kernel Size
Size of receptive field (1×1, 3×3, 5×5, 7×7)
📏 Stride
Step size for filter movement (1, 2, 3...)
🔲 Padding
Border pixels added to preserve spatial dimensions
Output Size Formula:
Output = ⌊(Input + 2×Padding - Kernel) / Stride⌋ + 1