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Basic CNN Architecture: How the 5 Layers Work Together

A Convolutional Neural Network (CNN) converts image data into predictions through layered processing. It starts with an input layer, followed by repeated convolutional layers with ReLU activation and pooling to extract hierarchical features. The output is flattened, passed through fully connected layers, and classified via an output layer like SoftMax, enabling CNNs to learn patterns from raw pixe

MK Gurucharan

09 Jan 2026 | 10 min read

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