Recurrent Neural Networks, Transformers, and Attention - MIT 6.S191 Lecture 2

This lecture from MIT's Introduction to Deep Learning course (6.S191) explores recurrent neural networks, transformers, and attention mechanisms. Presented by lecturer Ava Amini as part of the 2025 edition, the session provides a comprehensive overview of sequence modeling architectures in deep learning. Learn about the fundamental concepts behind RNNs, how they process sequential data, and their limitations. Discover how transformers and attention mechanisms have revolutionized natural language processing and other sequence-based tasks. The lecture includes theoretical foundations and practical applications of these powerful neural network architectures. For complete course materials including slides and labs, visit the official course website at introtodeeplearning.com.