Deep learning isn't hard anymore

This had the effect of bottlenecking deep learning, limiting it to the few projects that met those conditions. Over the last couple years, however, things have changed. The driver behind this growth is transfer learning. Transfer learning, broadly, is the idea that the knowledge accumulated in a model trained for a specific task--say, identifying flowers in a photo--can be transferred to another model to assist in making predictions for a different, related task--like identifying melanomas on someone's skin. Note: If you want a more technical dive into transfer learning, Sebastian Ruder has written a fantastic primer.