Google, IBM & Meta Certificates – 40% Off
One plan covers every Professional Certificate on Coursera.
Unlock All Certificates
Dive into the second part of this comprehensive lecture on Transformers architecture, exploring advanced concepts and implementations in this 79-minute presentation from the University of Utah Data Science program. Build upon foundational knowledge of attention mechanisms and self-attention to examine deeper architectural components, training strategies, and practical applications of transformer models. Access accompanying slides to follow along with detailed explanations of multi-head attention, positional encoding, layer normalization, and feed-forward networks within the transformer framework. Explore how these components work together to enable powerful natural language processing capabilities and understand the mathematical foundations that make transformers effective for sequence-to-sequence tasks, language modeling, and various downstream applications in machine learning and artificial intelligence.
