What is impossible today becomes feasible tomorrow thanks to data. Machine learning, particularly deep learning, has achieved advances in recent years, transforming fantasy into reality. These technological improvements are extraordinary, yet they rely on massive volumes of data being accessible. Furthermore, having data is insufficient for the type of machine learning called supervised learning. While powerful, supervised machine learning requires data with labels that are used to train and direct the algorithm as it learns.
Unfortunately, data does not come neatly organized with labels in the actual world. Enterprises acquire vast volumes of data, yet only a small percentage (if any) of it gets annotated. Attempts are made to manually annotate them to harness supervised learning potential, but this activity may be expensive, inefficient, and time-consuming. In this I will talk about how to train your machine learning models with limited labels.
