Convolutional Neural Networks (CNNs) are a specialized type of neural network particularly effective in tasks involving grid-like data such as images. They leverage convolutional layers to automatically learn spatial hierarchies of features from the input data. Unlike traditional neural networks, CNNs exploit the local dependencies present in images by applying small filters (kernels) across the entire image. These filters detect patterns like edges, textures, and shapes. The output of each convolution is a feature map, representing where those patterns are found. Multiple convolutional layers are stacked to learn increasingly complex features – for example, early layers might detect edges while later layers combine these into objects or parts of objects. Pooling layers reduce the spatial size of the representation, decreasing computational complexity and making the network more robust to variations in object position. Finally, fully connected layers classify the learned features into specific categories. CNNs are widely used in image classification, object detection, facial recognition, medical imaging analysis, and video processing due to their ability to automatically learn relevant features without manual engineering.