Neura saysCNNs use filters, pooling, and stride to extract and downsample features.
Three knobs shape a CNN. Filters (kernels) are the learnable feature detectors. Stride is how far the filter jumps each step (bigger stride → smaller output). Pooling (max/average) downsamples feature maps, shrinking spatial size while keeping the strongest signals, which adds robustness and cuts compute. Together they progressively compress the image into rich, abstract features.
Power-ups you unlock
Filters: learnable feature detectors
Stride: filter step size (controls output size)
Pooling: downsample, keep strongest signals
Progressively compress into abstract features
The Overfit Ogre attacks — common mistakes
Confusing stride with pooling
Over-pooling and losing spatial detail
Too-large filters losing locality
Boss battleExplain what max-pooling keeps and what it throws away.