A system-level overview of LLM architecture, including transformer blocks, encoder vs decoder models, attention mechanisms, training and inference flow, and how all components fit together in modern AI systems.
Understand the attention mechanism that powers Large Language Models (LLMs), including self-attention, query-key-value vectors, attention scores, multi-head attention, and why attention replaced recurrent neural networks.
Learn the key components of modern LLMs including tokenization, embeddings, transformers, attention, training, inference, and model parameters.