Convolutional Neural Networks (CNN) are a type of Artificial Neural Network (ANN) that is widely used in the field of Deep Learning. This type of network has many applications in image and video analysis, natural language processing, and many other areas. CNNs are based on the concept of convolution, which is a mathematical operation that combines two signals. The convolution operation allows the network to identify certain features of the input data, making it suitable for tasks such as image classification, object detection, segmentation, and classification of sounds.
A CNN typically consists of an input layer, several convolutional layers, one or more fully-connected layers, and an output layer. The input layer takes in an input vector, which is a representation of an image or other data. The convolutional layers apply a convolution operation on the input vector to extract certain features from it. The fully-connected layers then combine these extracted features before passing them to the output layer, which produces the desired output.
Typically, a CNN is trained using a variety of supervised learning techniques, such as backpropagation or supervised learning algorithms. During training, the weights of the CNN are adjusted so as to minimize an error metric for the given task.
CNNs are widely used in many applications, such as image classification, object detection, semantic segmentation, and natural language processing. CNNs have also been used in a wide variety of applications in computer vision and natural language processing. They have been used for tasks such as facial recognition, object detection, image classification, autonomous navigation, and autonomous driving.
In recent years, CNNs have become more powerful due to advances in computational power. They are now capable of recognizing highly complex patterns and performing tasks such as visual recognition and reasoning. These improvements have also enabled the use of CNNs in applications such as facial recognition and autonomous navigation.
