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Convolutional Neural Networks (CNNs) are a type of Artificial Neural Network (ANN) that are designed to process data with a grid-like topology, such as an image. They are a key component in the field of Machine Learning and Artificial Intelligence, and are widely used in Technology for applications like facial recognition, image analysis, and self-driving cars. CNNs are composed of layers that perform different operations on the input data. The first layer is typically a convolutional layer, which applies a set of filters to the input data to extract features such as edges, shapes, and textures. This layer is followed by activation functions, such as ReLU (Rectified Linear Unit) or sigmoid, which introduce non-linearity into the model. After that, pooling layers are used to reduce the spatial dimensions of the feature maps while retaining important information. The final layers of a CNN are typically fully connected layers, which perform classification or regression tasks based on the extracted features. CNNs differ from other ANNs in that they take advantage of the spatial structure of the input data. By using convolutional and pooling layers, CNNs can learn hierarchical feature representations that are translation invariant, meaning that the same feature can be detected regardless of its location in the input data. This property makes CNNs particularly well-suited for image and video processing tasks. In summary, Convolutional Neural Networks are a type of Artificial Neural Network used in Machine Learning and Artificial Intelligence technology to process grid-like topology data such as images. They are composed of convolutional, activation, pooling and fully connected layers which extract features, introduce non-linearity, reduce spatial dimensions and perform classification or regression tasks respectively. This allows CNNs to learn hierarchical feature representations that are translation invariant making them ideal for image and video processing tasks.