Self-supervised Learning: Pretext Tasks and BERT

This lecture covers the concept of self-supervised learning, where the model generates supervisory signals from the input itself to learn useful features. It explores various pretext tasks like predicting image rotation, colorization, and masked language modeling. The lecture also delves into BERT, a language model trained on masked language modeling, enabling bidirectional understanding of text sequences.