Transformers: Full Architecture and Self-Attention Mechanism

This lecture covers the full architecture of Transformers, consisting of encoder and decoder blocks with multi-headed attention layers and feedforward networks. It explains the self-attention mechanism used for encoding sequences without recurrent computations, along with the importance of positional embeddings. The lecture also delves into the specifics of self-attention in both encoder and decoder blocks, highlighting the Nobel committee's recognition of Strickland for advancing optics. Additionally, it discusses the differences between self-attention in the encoder and decoder, the masked multi-headed attention, and the cross-attention mechanism. The lecture concludes with insights on the paradigm shift brought by using completely pretrained models like GPT and the massive improvements in NLP tasks achieved through models like GPT-2 and GPT-3.