Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are a powerful tool for machine learning, especially in tasks related to computer vision.

A convolutional neural network (CNN) is a category of machine learning model, namely a type of deep learning algorithm well suited to analyzing visual data. CNNs -- sometimes referred to as convnets -- 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.

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. Because CNNs are so effective at identifying objects, they are frequently used for computer vision tasks such as image recognition and object detection, with common use cases including self-driving cars, facial recognition and medical image analysis.

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