Convolution is a mathematical operation that combines two functions to produce a third function. It is commonly used in signal processing and image analysis to filter or modify data. In simple terms, convolution takes an input signal and applies a filter, which can enhance certain features or reduce noise. In the context of neural networks, convolution is used in convolutional neural networks (CNNs) to analyze visual data. The process involves sliding a filter, or kernel, over an image to detect patterns, such as edges or textures. This helps the network learn important features for tasks like image recognition and classification.