Deep learning isn’t hard anymore by Caleb Kaiser via @TDataScience

Deep learning used to require large amounts of data, deep pockets, and a novel, usually custom-built, architecture. But with transfer learning (which takes a pre-trained model and retrains the last layers of the model to focus on a new task), a single engineer can deploy a model in a new domain in a matter of days

There is a great link in the article to a primer on Transfer Learning which is well worth the time investment in reading and learning so you can take advantage of that technique.

Deep learning is about to get easier by Ben Dickson via @VentureBeat

One problem with deep learning algorithms is that they require vast amounts of data. Fortunately, researchers have found workarounds that will level the playing field.

This is really interesting and anything that helps more people take advantage of AI has got to be a great thing (if it has been tested to make sure you can rely on the answers).