Transformers Explained: Attention, Math and How LLMs Really Work

Every modern language model is built from one idea: attention. In this episode we build a transformer from the ground up, with real numbers and visuals. We cover tokenization, embeddings, positional encoding, queries, keys and values, a full derivation of scaled dot-product attention including why we divide by the square root of d_k, multi-head attention, residual connections, layer norm, feedforward layers, causal masks, encoder versus decoder models, next-token training, fine-tuning, LoRA, inference cost, and the real limits of large language models. This is episode 29 of 30 in the Machine Learning from Zero to Advanced series. Next up: reinforcement learning, advanced topics, MLOps and capstone projects. 0:00 Intro 0:54 Tokens and Embeddings 2:37 Positional Encoding 3:32 Queries, Keys and Values 4:56 Deriving Scaled Attention 7:58 Multi-Head and the Block 10:12 Masks and Architectures 11:11 Training Language Models 12:59 Fine-Tuning and LoRA 14:29 Inference and Limits 16:12 Recap and Next Episode #Transformers #LLM #Attention #MachineLearning #DeepLearning