CNNs are generally used for image classification problems, but can also be exposed in Natural Language Processing (NLP), in conjunction with word vectors, because of their proven results. Unlike a regular neural network, a CNN will have additional layers such as convolutional layers and subsampling layers. Convolutional layers take input data (such as images) and apply convolution operations on top of them. You can think of it as applying a function to the input. Convolutional layers act as filters that pass a feature of interest to the upcoming subsampling layer. A feature of interest can be anything (for example, a fur, shade and so on in the case of an image) that can be used to identify the image. In the subsampling layer, the input from convolutional layers is further smoothed. So, we end up with a much smaller image resolution and reduced color contrast, preserving only the important information. The input is then passed on to fully connected layers. Fully connected layers resemble regular feed-forward neural networks.