🏗️ CNN Architecture Builder
Build and understand convolutional neural networks layer by layer
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Introduction to CNNs
Convolutional Neural Networks (CNNs) revolutionized computer vision by automatically learning spatial hierarchies of features. Instead of manually designing features, CNNs learn them directly from pixels.
🎯 Why CNNs?
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Spatial relationships: Pixels close together are more related than distant ones
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Translation invariance: A cat is a cat whether in the corner or center
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Parameter sharing: Same filter applied everywhere = fewer parameters
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Convolution
Extracts features like edges, textures, and patterns using learnable filters
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Pooling
Reduces spatial dimensions while keeping important features
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Dense Layers
Combines features for final classification or regression
📊 Typical CNN Architecture
Input:224×224×3 (RGB image)
Conv Layer:32 filters, 3×3 kernel → 224×224×32
MaxPool:2×2 pool → 112×112×32
Conv Layer:64 filters, 3×3 kernel → 112×112×64
MaxPool:2×2 pool → 56×56×64
Flatten:→ 200,704 features
Dense:128 units → Classification