 Examples of deep structures that can be trained in an unsupervised manner are neural history compressors and deep belief networks.Deep neural networks are generally interpreted in terms of the universal approximation theorem or probabilistic inference.The classic universal approximation theorem concerns the capacity of feedforward neural networks with a single hidden layer of finite size to approximate continuous functions.