Where does the "deep learning needs big data" rule come from When reading about deep learning I often come across the rule that deep learning is only effective when you have large amounts of data at your disposal. These statements are generally accompanied by a figure such as this:



The example (taken from https://hackernoon.com/%EF%B8%8F-big-challenge-in-deep-learning-training-data-31a88b97b282 ) is attributed to a 'famous slide from Andrew Ng'. Does anyone know what this figure is actually based upon? Is there any research that backs up this claim?
The original slide in question "Scale drives deep learning progress" is possibly what you currently can find at https://cs230.stanford.edu/files/C1M1.pdf (page 13). It may be roughly interpreted with "low bias learners [in that plot, larger neural networks] tend to benefit from more training examples".