Convolutional Neural Networks (CNN)

Convolutional Neural Networks (also known as CNNs) are a type of artificial neural network used in computer vision and machine learning. They are designed to identify features in images and other types of data.

CNNs are based on the idea of a convolutional layer, which takes an input image and passes it through a set of filters or kernels to identify specific features. For example, a convolutional layer can detect edges and shapes in an image. The output of the convolutional layer is a feature vector, which is used to identify objects and classify them.

The architecture of a CNN consists of convolutional layers, pooling layers, and fully connected layers. The convolutional layers are responsible for identifying the features in an image. The pooling layers are used to reduce the dimensionality of the feature vector. The fully connected layers are used to learn the relationships between the identified features and classes.

CNNs are commonly used in image related tasks, such as facial recognition, object detection, and image segmentation. They can also be used in natural language processing for sentiment analysis, text classification, and question-answering systems.

In addition to their applications in computer vision and natural language processing, CNNs have also been used in healthcare, robotics, autonomous vehicles, and data mining. The potential applications of CNNs are virtually limitless.

Convolutional Neural Networks are a powerful tool for computer vision and machine learning. By identifying patterns and features in images, they enable the development of countless vision-related applications, ranging from facial recognition to autonomous vehicle navigation.