How transfer learning can boost business efficiency
Transfer learning is a technique that’s risen to prominence in the AI and machine learning community over the past several decades. It refers to storing knowledge gained while solving one problem and applying it to a different, but related, problem. So far, transfer learning has been applied to cancer subtype discovery, video game playing, text classification, medical imaging, spam filtering, and more. Prominent computer scientist Andrew Ng said in 2016 that transfer learning will be one of the major drivers of machine learning commercial success.
Transfer learning has its benefits, chief among them allowing companies to repurpose machine learning models for new problems with less training data. But transfer learning is often simpler in theory than in execution. For example, models trained on one problem and applied to another can suffer from negative transfer, where the model becomes less accurate over time.