Transformers: Overview and Self-Attention

This lecture covers the overview of Transformers, focusing on the architecture, results, variants, and pretraining. It discusses the limitations of recurrent models, the concept of self-attention, and its hypothetical examples. The lecture explores the barriers and solutions for using self-attention as a building block, including position representation vectors through sinusoids and adding nonlinearities. It also delves into multi-headed attention, its computational efficiency, and the scaled dot product attention. The lecture concludes with a discussion on the Transformer decoder, encoder, and modifications, emphasizing the importance of pretraining models for natural language processing.