What is the difference between a convolutional layer and a pooling layer in Keras?

A convolutional layer in Keras is a type of layer that applies a convolution operation to the input, which is a mathematical operation that is used to detect patterns in data. This layer is typically used to extract features from images, such as edges, shapes, and textures. The convolutional layer is usually followed by a pooling layer, which is a type of layer that reduces the dimensionality of the data by combining the outputs of the convolutional layer. Pooling layers are used to reduce the computational complexity of the model and to reduce overfitting. The most common type of pooling layer is the max pooling layer, which takes the maximum value from each region of the input. Other types of pooling layers include average pooling and global pooling.