👉 Convolutional Neural Networks (CNNs) are a class of deep learning models specifically designed for processing structured grid data, such as images. They leverage convolutional layers to automatically and adaptively learn spatial hierarchies of features from input data, reducing the need for manual feature extraction. Each convolutional layer applies a set of learnable filters (kernels) to the input, capturing local patterns like edges and textures. These filters slide over the input, performing element-wise multiplications and summing the results to produce feature maps that highlight specific features. Pooling layers, often following convolutional layers, reduce the spatial dimensions of these feature maps, retaining only the most important information while decreasing computational complexity. This hierarchical feature extraction process enables CNNs to effectively recognize complex patterns and objects in images, making them highly effective for tasks like image classification, object detection, and segmentation.
