Convolutional Neural Networks (CNNs) are a type of regularized feed-forward neural networks that are capable of learning feature engineering through filters or kernels. This allows CNNs to excel in tasks involving image, speech, or audio signal inputs. CNNs have gained popularity due to their ability to prevent vanishing or exploding gradients that were common in earlier neural networks. By using regularized weights over fewer connections, CNNs can effectively optimize filters and prevent gradient-related issues during backpropagation.

One of the primary advantages of CNNs is their superior performance in image recognition tasks. CNNs are particularly effective in analyzing data with a grid-like structure, such as images. They can automatically learn relevant features from the input data and apply them to make accurate predictions. This makes CNNs highly suitable for tasks such as object detection, image segmentation, and image classification.

CNNs work by applying convolutional layers, which involve convolving filters over the input data. These filters detect specific features within the data, such as edges or textures. The output of the convolutional layer is then passed through activation functions, which introduce non-linearity into the network. Rectified Linear Unit (ReLU) activation function is commonly used in CNNs, as it helps with faster convergence and prevents the vanishing gradient problem.

In addition to convolutional and activation layers, CNNs also incorporate other types of layers such as pooling and fully connected layers. Pooling layers downsample the feature maps obtained from the convolutional layers, reducing the spatial dimensions while retaining important features. Fully connected layers connect every neuron from one layer to every neuron in the next layer, allowing the network to capture complex relationships.

CNNs have found applications in various fields, including computer vision, natural language processing, speech recognition, and medical image analysis. They have been used to develop advanced technologies such as self-driving cars, facial recognition systems, and image captioning systems. Furthermore, CNNs have been integrated into popular deep learning frameworks, making them easily accessible to developers and researchers.

In summary, Convolutional Neural Networks (CNNs) are regularized feed-forward neural networks that excel in tasks involving image, speech, or audio signal inputs. They learn feature engineering through filters and prevent gradient-related issues during backpropagation. CNNs are particularly effective in image recognition tasks and are widely used in computer vision, natural language processing, and other fields. They have revolutionized the field of deep learning and continue to be the backbone of many state-of-the-art machine learning applications.