A convolutional neural network (CNN) is a type of artificial neural network that is commonly used in computer vision tasks, such as image and video recognition. CNNs are designed to process data with a grid-like structure, such as an image, which makes them well-suited for tasks that involve recognizing patterns in visual data.

One of the key features of CNNs is the use of convolutional layers, which are designed to automatically learn the spatial hierarchies present in image data. In a convolutional layer, each neuron is connected to a small region of the input data, known as a receptive field. This allows the neurons in a convolutional layer to learn to detect specific patterns in the data, such as edges, corners, or textures.

Here are two examples of how CNNs are used:

Image recognition: CNNs are commonly used for image recognition tasks, such as classifying an image as containing a dog, cat, or car. In this case, the input to the CNN is an image, and the output is a prediction of what is contained in the image. The convolutional layers of the CNN are responsible for learning to detect the various features of the objects in the image, such as the shape of a dog’s ears or the texture of a car’s tire.

Object detection: CNNs can also be used for object detection, which involves not only recognizing the objects in an image, but also localizing them in the image. In this case, the output of the CNN is not only a prediction of the objects in the image, but also a set of bounding boxes that indicate the location of each object. The convolutional layers of the CNN are responsible for learning to detect the objects and their locations in the image.

In summary, CNNs are a powerful tool for tasks involving pattern recognition in visual data. Their ability to automatically learn spatial hierarchies in data makes them well-suited for a variety of computer vision tasks, such as image and object recognition.