Stanford University offers a comprehensive course on the Transformer deep learning architecture and its recent advancements. This course, Stanford CS25: Transformers United, is freely available on YouTube, featuring 33 video lectures by leading academics and industry experts. The course covers the latest breakthroughs in AI, including applications in computer vision, reinforcement learning, and extensions like Mixture of Experts (MoE) architectures. Benefits: - Access to lectures by top experts in the field. - Comprehensive understanding of Transformer architectures. - Practical insights into the latest research and applications. Syllabus Overview: 1. Introduction to Transformers: Overview of DL models revolutionizing NLP, CV, RL. 2. Transformers in Language: Development of GPT Models, GPT-3. 3. Transformers in Vision: Tackling problems in Computer Vision. 4. Decision Transformer: Reinforcement Learning via Sequence Modeling. 5. Mixture of Experts (MoE): Paradigm and Switch Transformer. 6. DeepMind's Perceiver: New data family architecture. 7. Self Attention and Non-parametric Transformers (NPTs). 8. Transformer Circuits: Induction Heads, In-Context Learning. 9. Transformers for Audio Applications: Audio, Speech, Music. 10. Hierarchies in Neural Networks: Representing part-whole hierarchies. 11. Emergent Abilities and Scaling in LLMs. 12. Biomedical Transformers. 13. Neuroscience-Inspired AI. 14. Generalist Agents in Open-Ended Worlds. 15. Training Helpful Chatbots. 16. Machine Translation: Scaling Human-Centered Machine Translation. 17. Retrieval Augmented Language Models. 18. Aligning Open Language Models. 19. Behind the Scenes of LLM Pre-training.