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.
Course Overview
What You Will Build Toward
- Navigate the Transformer Architecture Deep Dive learning path across 10 chapters.
- Choose the right chapter based on your current goal and prerequisites.
- Move from overview material into the canonical chapter experience.
Chapter Path
Start With Any Chapter
Before You Start
Recommended Background
- Working knowledge of the course category.
- Willingness to work through examples and short checks.
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
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
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
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
Chapter 5: Feed-Forward Networks
Processing After Attention
- FFN architecture in transformers
- Two linear transformations
- ReLU activation
- Dimension expansion (4x rule)
- FFN implementation
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
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
Chapter 8: Decoder Architecture
Generating Sequences
- Decoder layer components
- Masked self-attention
- Encoder-decoder attention
- Causal masking explained
- Complete decoder implementation
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
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