Скачать или смотреть Why Transformer Blocks Are So Complicated

Every modern large language model, GPT, LLaMA, Mistral, and others, is built by stacking the same fundamental unit: the Transformer block.

In this video, we break down exactly what happens inside a single Transformer block, step by step, and explain how its components work together to turn token embeddings into contextual representations.

We cover the three core building blocks of the architecture:

Multi-Head Self-Attention: how tokens exchange information.
Feed-Forward Networks (FFN): how features are transformed independently per token.
Residual Connections and Layer Normalization: why deep Transformers are stable and trainable.

Rather than treating the Transformer as a black box, this video explains the data flow, equations, and design choices that make the architecture scalable and effective.

Topics covered:

Input and output shapes inside a Transformer block
Where attention fits in the computation pipeline
Why residual connections are necessary for deep models
How LayerNorm stabilizes training
How stacking blocks leads to emergent reasoning behavior