Transfer learning from multiple pre-trained computer vision models

Note: Please access the full code in this GitHub repo. The multitude of methods jointly referred to as "deep learning" have disrupted the fields of machine learning and data science, rendering decades of engineering know-how almost completely irrelevant--or so common opinion would have it. Of all these, one method that stands out in its overwhelming simplicity, robustness, and usefulness is the transfer of learned representations. Especially for computer vision, this approach has brought about unparalleled ability, accessible to practitioners of all levels, and making previously insurmountable tasks as easy as from keras.applications import *. Put simply, the method dictates that a large data set should be used in order to learn to represent the object of interest (image, time-series, customer, even a network) as a feature vector, in a way that lends itself to downstream data science tasks such as classification or clustering.