This tutorial will look at how deep learning methods can be applied to problems in computer vision, most notably object recognition. It will start by motivating the need to learn features, rather than hand-craft them. It will then introduce several basic architectures, explaining how they learn features, and showing how they can be "stacked" into hierarchies that can extract multiple layers of representation. Throughout, links will be drawn between these methods and existing approaches to recognition, particularly those involving hierarchical representations. The final part of the lecture will examine the current performances obtained by feature learning approaches on a range of standard vision benchmarks, highlighting their strengths and weaknesses. The tutorial will conclude with a discussion of vision problems that have yet to be successfully addressed by deep learning.