The chapter is devoted at illustrating the basic principles and the current results which characterize the research on Deep Learning. The term refers to the theory and practice of devising and training complex neural networks for supervised and unsupervised tasks. Within the article, we illustrate the basic principle underlying the idea of a single neural unit, and will show how these units can be combined to realize a complex network. We shall discuss the basic algorithms for training a network and the recent advances proposed by the literature for scaling up the training to deep architectures. The article concludes by an overview of the most successful deep architectures proposed in the literature, both for supervised and unsupervised learning