Convolutional Neural Network (CNN)

A Convolutional Neural Network (CNN) is a type of deep learning model specifically designed for processing structured grid data, such as images. CNNs are characterized by their use of convolutional layers, which apply filters to the input data to capture spatial hierarchies and patterns. These convolutional layers are typically followed by pooling layers that reduce the dimensionality of the data, helping to extract essential features while maintaining computational efficiency.CNNs are particularly effective in image recognition and classification tasks, as they are capable of learning features such as edges, textures, and shapes at various levels of abstraction. They consist of multiple layers, including convolutional layers, activation functions such as ReLU (Rectified Linear Unit), pooling layers, and fully connected layers, which ultimately produce the output.CNNs leverage the local connectivity and shared weights of convolutional filters, which allows them to require fewer parameters compared to fully connected neural networks, making them efficient for large inputs like images. They have become the foundational architecture for many applications in computer vision, natural language processing, and other fields where spatial or temporal data patterns are essential.