A convolutional neural network (CNN) is a type of artificial neural network USED in image recognition and processing that is specifically designed to process pixel data. In deep LEARNING, a CNN is a class of deep neural nets, most commonly applied to analysing visual imagery. CNNs use a variation of multilayer perceptrons designed to require minimal pre-processing. Convolution is the process of filtering through the image for a specific pattern.

CNNs typical has the following layers other than Input and Output layers –

• Convolutional Layer (CONV)
• Rectifier Linear Unit Layer (RELU)
• Pooling Layer (POOLING)

There is also a fully CONNECTED layer (FC) at the end prior to output layer, in order to EQUIP net with the ability to classify data samples.

A fundamental architecture comprising of all layers for a CNN can be described in the image below. This is an illustrative structure and layers can be used differently to solve a specific problem based on a context or situation.

Yes, CNN does perform dimensionality reduction. Pooling layer is used for this.