It's not always the data. Share some love with model architecture too.

Disclaimer: This post may be slightly dryer and technical than my usual posts, but I'll try my best to simplify and make it less painful to read. Conventional wisdom tells us when it comes to Machine/Deep Learning you need large quantity of data. While this is true, in situations where large quantity of relevant data is not readily available, it shouldn't prevent you from pursuing to solve a problem using Deep Learning. It should be evaluated on a per-case basis. Don't just rule out adopting Machine/Deep Learning because "there is not enough data".