Convolutional Neural Networks (CNNs) are a type of neural network that are particularly well-suited for image and video processing. They are designed to recognize patterns and features in images, making them useful for tasks such as image classification, object detection, and segmentation.
The key feature of CNNs is their use of convolutional layers. Convolution involves sliding a small matrix called a filter or kernel over an input image and performing a mathematical operation to produce a new output image. By applying convolutional layers, CNNs can learn to recognize features such as edges, corners, and textures in an image.
In addition to convolutional layers, CNNs also typically include pooling layers, which reduce the spatial dimensionality of the data by downsampling the feature maps. This helps to reduce the computational complexity of the network and prevent overfitting.
CNNs are often composed of multiple layers, including convolutional layers, pooling layers, and fully connected layers, which allow the network to learn more complex features and make more accurate predictions. During training, the network adjusts the weights of the connections between nodes to minimize the error between the predicted output and the actual output.
CNNs have been very successful in a variety of applications, including image recognition, object detection, and segmentation. They are used in a wide range of fields, including computer vision, robotics, and self-driving cars.