A convolutional neural network (CNN) is a class of deep, feed-forward artificial neural networks that has successfully been applied to analyzing visual imagery. CNNs use a variation of multilayer perceptrons designed to require minimal preprocessing.
Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex.
Individual cortical neurons respond to stimuli only in a restricted region of the visual field known as the receptive field. The receptive fields of different neurons partially overlap such that they cover the entire visual field.
CNNs use relatively little pre-processing compared to other image classification algorithms. This means that the network learns the filters that in traditional algorithms were hand-engineered. This independence from prior knowledge and human effort in feature design is a major advantage.
CNN’s have applications in image and video recognition, recommender systems and natural language processing.
My vote goes to Convolutional Networks. Strictly speaking not an algorithm but a model. The recent surge of interest in deep learning is due to the immense popularity and effectiveness of convnets.
Prof. Max Welling
University of Amsterdam
Social tagging: deep learning