How Convolutional Layers Work in Deep Learning Neural Networks?

In deep learning, convolutional layers have been major building blocks in many deep neural networks. The design was inspired by the visual cortex, where individual neurons respond to a restricted region of the visual field known as the receptive field. A collection of such fields overlap to cover the entire visible area. Though convolutional layers were initially applied in computer vision, its shift-invariant characteristics have allowed convolutional layers to be applied in natural language processing, time series, recommender systems, and signal processing. The easiest way to understand a convolution is by thinking of it as a sliding window function applied to a matrix.