The scenario
An ML student documenting their image-classification CNN architecture for a course report needs a block diagram.
What is in this drawing
Read the decisions behind it.
01
Batch normalization after each conv layer inserted before pooling to stabilize training.
02
Dropout 0.5 between dense layers is the primary regularization mechanism to prevent overfitting.
03
Softmax output normalizes logits to class probabilities; replace with sigmoid for binary classification.
Add skip connections (ResNet-style) from conv1 to conv2 to combat vanishing gradients in deeper variants.
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