A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process data that has a grid-like topology, such as images. It applies a convolution operation to the input image, passing the result through multiple layers of neurons. The convolution operation extracts features from the input image, which are then used to make a prediction. For example, a CNN can be used to recognize objects in an image.
A recurrent neural network (RNN) is a type of artificial neural network used in sequence-based data processing. It is designed to process data that has a temporal or sequential structure, such as text, audio, video, and time series data. It applies a recurrent operation to the input data, passing the result through multiple layers of neurons. The recurrent operation captures the temporal dependencies in the input data, which are then used to make a prediction. For example, an RNN can be used to generate text from a given input.