This short tutorial on deep learning will review a variety of methods for learning multi-level, hierarchical representations, emphasizing their common traits. While deep architectures have theoretical advantages in terms of expressive power and efficiency of representation, they also provide a possible model for information processing in the mammalian cortex, which seems to rely on representations with multiple levels of abstractions. A number of deep learning methods have been proposed since 2005, that have yielded surprisingly good performance in several areas, particularly in vision (object recognition), and natural language processing. They all learn multiple levels of representation using some form of unsupervised learning. Hypotheses to explain why these algorithms work well will be discussed in the light of new experimental results. Many of these algorithms can be cast in the framework of the energy-based view of unsupervised learning, which generalizes graphical models used as building blocks for deep architectures, such as the Restricted Boltzmann Machines (RBM) and variations of regularized auto-encoders. Old and new algorithms will be presented for training, sampling, and estimating the partition function of RBMs and Deep Belief Networks. Applications of deep architectures to computer vision and natural language processing will be described. A number of open problems and future research avenues will be discussed, with active participation from the audience.