Chapter 8: Self-Supervised Learning

Since the dawn of Machine Learning, the field has been neatly divided into two camps: supervised learning and unsupervised learning. In supervised learning, there should be a labeled dataset available, and if that is not the case, then the only option left is unsupervised learning. While unsupervised learning may sound great as it can work without labels, in practice, the applications of unsupervised methods such as clustering are quite limited. There is also no easy option to evaluate the accuracy of unsupervised methods or to deploy them.

The most practical Machine Learning applications tend to be supervised learning applications (for example, recognizing objects in images, predicting future stock prices or sales, or recommending the right movie to you on Netflix). The trade-off for supervised learning is the necessity for well-curated and high-quality trustworthy labels. Most datasets are not born with labels and getting such labels can be...