Deep learning algorithms offer a powerful means to automatically analyze the content of biomedical images. However, many biological samples of interest are difficult to resolve with a standard optical microscope. Either they are too large to fit within the microscope's field-of-view, or too thick, or are quickly moving around. In this talk, I will discuss our recent work in addressing these challenges by using deep learning algorithms to design new experimental strategies for microscopic imaging. Specifically, we use deep neural networks to jointly optimize the physical parameters of our computational microscopes - their illumination settings, lens layouts and data transfer pipelines, for example - for specific tasks. Examples include learning specific illumination patterns that can improve classification of the malaria parasite by up to 15%, and establishing fast methods to automatically track moving specimens across gigapixel-sized images.