👉 Convolutional Neural Networks (CNNs) are a class of deep learning models specifically designed for processing grid-like data, such as images. The core mathematical operation in CNNs is the convolutional layer, which applies a set of learnable filters (kernels) to the input data. Each filter performs element-wise multiplication with a localized region of the input and sums the results, generating a feature map that highlights specific patterns or features in the input. This process is repeated across multiple filters and layers, allowing the network to learn hierarchical representations of the data. Additionally, pooling layers, often used after convolutional layers, apply downsampling operations (like max pooling) to reduce spatial dimensions and computational complexity while retaining important features. The combination of these mathematical operations enables CNNs to effectively extract and learn spatial hierarchies, making them powerful tools for tasks like image classification, object detection, and segmentation.
