The Transformer Block — Attention, Feed-Forward, Residuals & LayerNorm | datarekha

Assemble the full unit. Each block has two main parts — multi-head attention (tokens talk and mix context) and a per-token feed-forward network (it thinks) — each wrapped in a residual connection (gradients flow straight back) and layer normalization (numbers stay well-behaved). Attention, add, normalize; feed-forward, add, normalize. Stack it dozens of times and you have the body of a modern transformer. Chapter 64 of the full "ML & DL from scratch, with the math" course (watch the complete ~2h09m film, with all chapters & timestamps in its pinned comment). More at datarekha.com. Narration uses a synthetic AI voice.

Related free lessons on datarekha.com:
- Inside the transformer block: https://datarekha.com/deep-learning/transformer-block
- The Transformer Architecture: https://datarekha.com/deep-learning/the-transformer

Видео The Transformer Block — Attention, Feed-Forward, Residuals & LayerNorm | datarekha канала datarekha