Deep neural networks have recently conquered a number of major problems in machine perception. Able to learn representations, the methods are especially effective in domains where any given feature in the raw data is uninformative. Consider, for example, an image with dimension 300x300pixels. Any one pixel by itself effectively meaningless. By computing successive layers of representation, however, a neural network is able to identify hierarchical features, like edge detectors at a low level, and faces at a high level which enable accurate classification of images into abstract object categories.[……]
Józefowicz, R., Zaremba, W., & Sutskever, I. An Empirical Exploration of Recurrent Network Architectures. ICML, pp. 2342–2350, 2014.