  Deep learning owes its success to three key factors: scale of data, enhanced
models to learn representations from data, and scale of computation. This book
chapter presented the importance of the data-driven approach to learn good
representations from both big data and small data. In terms of big data, it has
been widely accepted in the research community that the more data the better
for both representation and classification improvement. The question is then
how to learn representations from big data, and how to perform representation
learning when data is scarce. We addressed the first question by presenting CNN
model enhancements in the aspects of representation, optimization, and
generalization. To address the small data challenge, we showed transfer
representation learning to be effective. Transfer representation learning
transfers the learned representation from a source domain where abundant
training data is available to a target domain where training data is scarce.
Transfer representation learning gave the OM and melanoma diagnosis modules of
our XPRIZE Tricorder device (which finished $2^{nd}$ out of $310$ competing
teams) a significant boost in diagnosis accuracy.
