Transformer Architecture Explained from Scratch — Attention, KV Cache & Embeddings (LLM Beginner)

Ever wondered what actually happens inside an LLM? This is the beginner tier of a three-part deep dive into Transformer and LLM architecture internals built from zero, no prior deep-learning background assumed. We start with what a token even is, build embeddings, derive self-attention(softmax(QKᵀ/√d)V) from the 2017 "Attention Is All You Need" paper, then assemble a full Transformer block and the KV cache that makes generation fast. Every episode follows the same rhythm: background with real papers, the mechanism derived, runnable PyTorch, a non-obvious insight, and an animated diagram. *What you'll learn* Tokens and embeddings: how text becomes vectors Language modeling as next-token prediction (and why it's compression) Attention, from intuition to the exact formula Multi-head attention and the full Transformer block The KV cache and positional encodings