Convolutional Neural Networks (CNNs) are a class of deep learning models specifically designed for image processing tasks. They operate using multiple layers: convolutional layers, pooling layers, and fully connected layers.
Convolutional Layer: Applies a set of learnable filters (kernels) to input images, extracting features such as edges, shapes, and textures. Each filter moves across the image, performing element-wise multiplication and summation (convolution operation).
Pooling Layer: Reduces spatial dimensions using max pooling or average pooling, preserving important features while reducing computational complexity.
Fully Connected Layer: Flattens the output and connects all neurons, allowing the network to make predictions or classifications.
CNNs are widely used for object detection, image segmentation, and face recognition due to their ability to learn hierarchical image features efficiently.