Transformers: Self-Attention and MLP

This lecture introduces the concept of transformers, focusing on self-attention and multi-layer perceptron (MLP) mechanisms. The instructor explains how transformers can process sequences efficiently, covering topics such as sequence-to-sequence transformation, positional encoding, and multi-head self-attention. The lecture delves into the architecture of transformers, their application in various modalities, and their ability to capture long-range dependencies. The instructor also discusses the scalability and parallelizability of self-attention, highlighting the advantages and challenges of using transformers in machine learning tasks.