Neural Networks: Transformers

This lecture covers the concept of transformers in neural networks, focusing on sequence-to-sequence transformations. It explains how various data types like words, images, and multimodal data can be represented as sequences and processed using transformers. The lecture delves into the architecture of transformers, including self-attention mechanisms, multi-head self-attention, and the importance of positional information. It also discusses the role of transformers in tasks like sentiment classification, translation, and image description. The presentation concludes with insights on the vision transformer architecture and the capabilities of transformers in capturing long-range dependencies.