Recycling Deep Learning Models with Transfer Learning

Deep learning models are indisputably the state of the art for many problems in machine perception. Using neural networks with many hidden layers of artificial neurons and millions of parameters, deep learning algorithms exploit both hardware capabilities and the abundance of gigantic datasets. In comparison, linear models saturate quickly and under-fit. But what if you don't have big data? Say you have a novel research problem, such as identifying cancerous moles given photographs.