Self-Supervised Learning

Self-supervised learning is a type of machine learning where the model learns from unlabeled data by creating its own supervisory signals. This is achieved by masking parts of the input data and training the model to predict the masked portions based on the remaining data. In essence, the data itself provides the labels, allowing the model to learn useful representations without requiring explicit human annotation.

Visit the following resources to learn more:

• @article@What Is Self-Supervised Learning?
• @video@What is Self Supervised Learning?
• @video@Mark Zuckerberg: AI Learns More Efficiently With Self-Supervised Learning