Learning is essential for building intelligent systems, whether carbon-based or silicon-based ones. Moreover these systems do not solve complex tasks in a single step but rather use multiple processing stages. Hence the question of deep learning, how efficient learning can be implemented in deep architectures. This fundamental question not only impinges on problems of memory and intelligence in the brain but is also at the forefront of current machine learning research. In the last year alone, new performance breakthroughs were achieved by deep learning methods in applications areas ranging from computer vision, to speech recognition, to natural language understanding, to computational biology. This talk will provide a brief overview of deep learning, from its biological origins to some of the latest theoretical, algorithmic, and application results.