Introduction to Active Learning

As data gets cheaper and cheaper to collect and store, data scientists are left with more data to deal with that they will ever be capable of analyzing. And this trend doesn't show signs of slowing down: the explosion of IoT devices paired with the appearance of new memory-greedy data formats are leaving data professionals fending for themselves in an ocean of raw data. Given that the most exciting advances in machine learning require large volumes of data, this is an exciting time. But it also raises a brand new challenge for the ML community: unless the data is labelled, it remains essentially useless for all ML applications relying on a supervised learning approach. Some of the most promising advances in AI over the last decade have come from the usage of deep learning models.