Convolutional Neural Network

What Convolutional Neural Network is

CNNs exploit locality and translation invariance: a feature detector that finds an edge is useful anywhere in the image, so weights are shared across positions.

How it works

Convolution layers apply filter banks, pooling layers downsample, and stacked blocks build from edges to textures to object parts. Residual connections allow very deep versions to train stably.

Why it matters

CNNs launched the deep learning era in vision and remain strong, efficient choices for many production image tasks even where transformers now lead on large benchmarks.

Common uses

• →Image classification and detection
• →Medical imaging
• →Industrial visual inspection
• →Video frame analysis