Cheers for the assist, but I really did mean that it's impossible to train deep learning with small datasets (also, I'm a "she" :).

yorwba below explains why transfer learning is not a way to train a deep net with small data sets. Rather, it's a way to re-train an already trained deep net. If you don't have a deep net trained on your target domain, then what?

Software devs are used to having access to libraries that make our lives simple. You don't have to invent a new language and write a compiler for it everytime you want to code, because java etc. That is the hope of services like the one discussed above.

However, because trained deep nets are very task-specific and it's virtually impossible for a small developer team with few resources to train one from scratch to reach state-of-the-art performance, developers who choose to use pre-trained deep nets as a library are perpetually dependent on Google, or whatever big company decides to offer its deep nets as a service, to determine the range of uses of their re-trained nets.