WHAT IS THE CNN?

A convolutional neural network (CNN) is a category of machine learning model. Specifically, it is a type of deep learning algorithm that is well suited to analyzing visual data. CNNs are commonly used to process image and video tasks. And, because CNNs are so effective at identifying objects, they are frequently used for computer vision tasks, such as image recognition and object recognition, with common use cases including self-driving cars, facial recognition and medical image analysis.

Older forms of neural networks often needed to process visual data in a gradual, piece-by-piece manner -- using segmented or lower-resolution input images. A CNN's comprehensive approach to image recognition enables it to outperform a traditional neural network on a range of image-related tasks and, to a lesser extent, speech and audio processing.

CNN architecture is inspired by the connectivity patterns of the human brain -- in particular, the visual cortex, which plays an essential role in perceiving and processing visual stimuli. The artificial neurons in a CNN are arranged to efficiently interpret visual information, enabling these models to process entire images.

CNNs also use principles from linear algebra, particularly convolution operations, to extract features and identify patterns within images. Although CNNs are predominantly used to process images, they can also be adapted to work with audio and other signal data.