Convolutional Neural Network: Definition and Applications

Convolutional Neural Networks (CNNs) are a type of artificial neural network that are particularly well-suited for processing data that has a grid-like topology, such as images. CNNs are used in a variety of applications, including image recognition, object detection, and natural language processing.

CNNs are characterized by their use of convolutional layers, which are designed to extract features from the input data. These convolutional layers apply a filter to the input data, which is a small matrix of weights that is slid across the input data. The filter is able to detect specific features in the input data, such as edges, corners, and textures.

The output of the convolutional layers is then passed to a series of pooling layers. Pooling layers reduce the dimensionality of the data by downsampling the output of the convolutional layers. This helps to reduce the number of parameters in the network and makes it more efficient.

Finally, the output of the pooling layers is passed to a fully connected layer, which is a traditional neural network layer that is used to classify the input data.

CNNs have achieved state-of-the-art results on a variety of tasks, including image recognition, object detection, and natural language processing. They are a powerful tool for machine learning, and their use is only expected to grow in the future.