Neura saysCNNs (convolutional neural networks) excel at images by learning local visual features.
A CNN slides small learnable filters across an image, detecting local patterns (edges, then textures, then objects) and building up hierarchy layer by layer. Sharing the same filter across the whole image (weight sharing) makes them efficient and translation-invariant. CNNs powered the 2012 deep-learning revival (AlexNet) and still dominate many vision tasks.
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Filters slide over the image (convolution)
Learn edges → textures → objects (hierarchy)
Weight sharing → efficient, translation-invariant
Drove the 2012 vision breakthrough
The Overfit Ogre attacks — common mistakes
Using a CNN where a transformer now wins
Forgetting input normalization
Ignoring receptive-field size
Boss battleExplain how weight sharing makes CNNs efficient and shift-invariant.