What is CNN?
Convolutional Neural Network
It is a deep learning architecture inspired by biological visual systems, developed to analyze visual data and images.
Overview
Convolutional Neural Networks (CNNs) are layered artificial intelligence models that automatically detect patterns such as edges, textures, and shapes in images. They extract prominent features by scanning pixel matrices in images using filters. It is one of the fundamental standards in object recognition, image classification, and medical image analysis.
How it works
The input image is passed through convolutional layers to extract small feature maps. Pooling layers downscale the data, and finally, fully connected layers predict what the image is.
Where it is used
It is widely used in autonomous vehicle lane tracking, face recognition in security cameras, and X-ray analysis in the healthcare sector.
Commonly confused with
Unlike text-focused Transformer models, it is designed to process 2D and 3D visual data grids directly.
Questions and answers
Are CNN models only used for images?
Although primarily used for images, it also yields successful results on other grid-structured data such as sound waves and time series.
What is the difference between a CNN and traditional artificial neural networks?
CNNs use convolution filters to preserve pixel relationships and spatial locations, thereby achieving much higher success rates in images.
Related terms
This explanation was written in plain language for TreScout with AI and machine-translated from the Turkish original · the Turkish version prevails. If something looks wrong or missing, write to hello@trescout.com. Read in Turkish →