Transformer Architecture Deep Dive

Master the Transformer architecture that revolutionized NLP. 10 comprehensive chapters covering attention mechanisms, self-attention, multi-head attention, positional encoding, encoder-decoder architecture, and implementation details with extensive formulas, code examples, and visual explanations.

Chapter 1: The Attention Mechanism

Foundation of Modern NLP

  • Why attention? Limitations of RNNs/LSTMs
  • Attention in sequence-to-sequence models
  • Query, Key, Value (QKV) concept
  • Attention scores and weights
  • Mathematical formulation
Attention Foundation Theory

Chapter 2: Self-Attention Mechanism

Attention is All You Need

  • Self-attention vs attention
  • Computing attention within a sequence
  • Scaled dot-product attention formula
  • Understanding attention weights
  • Self-attention implementation
Self-Attention QKV Implementation

Chapter 3: Multi-Head Attention

Learning Multiple Relationships

  • Why multiple attention heads?
  • Parallel attention computation
  • Head specialization (syntax, semantics, etc.)
  • Concatenation and linear projection
  • Multi-head attention implementation
Multi-Head Parallel Specialization

Chapter 4: Positional Encoding

Adding Order Information

  • Why positional encoding is needed
  • Sinusoidal positional encoding
  • Learned positional embeddings
  • Position encoding formulas
  • Relative vs absolute positions
Position Encoding Sinusoidal

Chapter 5: Feed-Forward Networks

Processing After Attention

  • FFN architecture in transformers
  • Two linear transformations
  • ReLU activation
  • Dimension expansion (4x rule)
  • FFN implementation
FFN Linear ReLU

Chapter 6: Residual Connections & Layer Normalization

Stabilizing Deep Networks

  • Residual (skip) connections
  • Why residuals help training
  • Layer normalization vs batch normalization
  • Pre-norm vs post-norm
  • Add & Norm implementation
Residual Normalization Stability

Chapter 7: Encoder Architecture

Understanding the Encoder Stack

  • Encoder layer components
  • Stacking encoder layers
  • Information flow through layers
  • What each layer learns
  • Complete encoder implementation
Encoder Stack Layers

Chapter 8: Decoder Architecture

Generating Sequences

  • Decoder layer components
  • Masked self-attention
  • Encoder-decoder attention
  • Causal masking explained
  • Complete decoder implementation
Decoder Masking Generation

Chapter 9: Complete Transformer Architecture

Putting It All Together

  • Full encoder-decoder transformer
  • Input/output embeddings
  • End-to-end forward pass
  • Training the transformer
  • Complete PyTorch implementation
Complete Architecture Implementation

Chapter 10: Transformer Variants & Optimizations

Beyond the Original

  • Encoder-only models (BERT)
  • Decoder-only models (GPT)
  • Efficient transformers (Linformer, Performer)
  • Sparse attention patterns
  • Recent architectural improvements
Variants BERT GPT