The Transformer Architecture

The Transformer architecture, introduced in “Attention Is All You Need” (2017), revolutionized natural language processing by eliminating the need for recurrence and convolutions while enabling efficient parallel processing and better handling of long-range dependencies.

Attention Mechanisms and Self-Attention

• Attention Basics
  • Query, Key, Value paradigm
  • Attention scores computation
  • Softmax normalization
  • Output computation
• Self-Attention
  • Token-to-token relationships
  • Parallel computation
  • Global context capturing
  • Attention masks

Multi-Head Attention and Positional Encodings

• Multi-Head Attention
  • Multiple attention heads
  • Different representation subspaces
  • Head concatenation
  • Linear transformation
• Positional Encodings
  • Sine and cosine functions
  • Absolute position information
  • Learned vs. fixed encodings
  • Position representation

Transformer Encoder and Decoder Stacks

• Encoder Architecture
  • Self-attention layers
  • Feed-forward networks
  • Layer stacking
  • Information flow
• Decoder Architecture
  • Masked self-attention
  • Cross-attention mechanism
  • Auto-regressive processing
  • Output generation

Residual Connections and Layer Normalization

• Residual Connections
  • Skip connections
  • Gradient flow
  • Deep network training
  • Feature preservation
• Layer Normalization
  • Normalization strategy
  • Training stability
  • Feature scaling
  • Batch independence

Learning Resources

Original Paper and Explanations

• Attention Is All You Need
• The Illustrated Transformer
• The Annotated Transformer

Deep Dives

• Stanford CS224n: Transformers Lecture
• Transformer Architecture: The Positional Encoding
• Understanding Layer Normalization

Implementation Guides

• HuggingFace Transformers Documentation
• PyTorch Transformer Tutorial
• TensorFlow Transformer Tutorial

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