Learn about Rotary Position Embedding (RoPE) in this 40-minute technical video that breaks down complex concepts into simple terms for understanding how self-attention works in Transformers with relative position encoding. Explore the mathematical foundations and practical applications of RoPE that enable Large Language Models (LLMs) to handle extended context lengths up to 100K tokens. Dive into the key concepts from the RoFormer paper, examining how rotary position embeddings enhance transformer architectures for improved performance in natural language processing tasks. Gain valuable insights into this advanced AI research topic through clear explanations and detailed breakdowns of the underlying mechanisms.