A Convolutional Neural Network (CNN) is a deep learning algorithm primarily used for analyzing visual data, such as images and videos. It consists of layers that perform convolution operations to automatically detect features like edges, textures, and patterns in the data. Key components of CNN: Convolutional Layer: Applies filters (kernels) to input data to extract features. Activation Function (ReLU): Introduces non-linearity, allowing the model to learn complex patterns. Pooling Layer: Reduces the spatial size of feature maps (e.g., max pooling), which reduces computation and helps prevent overfitting. Fully Connected Layer: At the end, connects the extracted features to the output for classification or regression tasks. Applications: Image classification, object detection, facial recognition, and more. CNNs are effective because they can learn hierarchical features, making them ideal for visual data processing.