Transformer Architecture

Attention Is All You Need

45 SLIDES Part 2: Core Architectures

From Sequential to Parallel: RNNs process words one-by-one like reading a book. Transformers process all words simultaneously like seeing a photograph. This parallelization made GPT and BERT possible - training that took months now takes days.

Prerequisites

Week 4: Attention mechanism fundamentals
Matrix operations (multiplication, softmax)
Understanding of parallel vs sequential processing

Overview

The architecture that revolutionized NLP. Self-attention, multi-head attention, and positional encoding.

Learning Objectives

Explain why transformers replaced RNNs (parallelization advantage)
Calculate self-attention scores using Query, Key, Value
Understand multi-head attention and its benefits
Describe positional encoding and why it's necessary
Draw the complete transformer encoder-decoder architecture

Key Topics

Self-attention

Multi-head attention

Positional encoding

Feed-forward layers

Key Concepts

Self-attentionEach position attends to all positions in sequence

Query, Key, Value (QKV)The three projections for attention

Scaled dot-product attentionQK^T / sqrt(d_k) then softmax

Multi-head attentionMultiple attention patterns in parallel

Positional encodingInject position information (no recurrence)

Feed-forward networkPosition-wise fully connected layers

Key Visualizations

Sr 16 Transformer Architecture Annotated

3D Multihead Attention

Attention Heatmap