Module 3: The Transformer Architecture

Welcome to Module 3: The Transformer Architecture. In this module, we assemble the individual components—Self-Attention, Layer Normalization, Residual Connections, and Feed-Forward Networks—into a complete, scalable deep learning block.

Concepts in this Module

• Concept 01: Residual Skip Connections & RMSNorm
  • The Everyday Problem: When stacking 96 transformer layers on top of each other, how do gradients and information flow cleanly through the network without exploding or vanishing to zero?
  • Code & Math: The residual additive shortcut x_next = x + SubLayer(x) and Root Mean Square Normalization (RMSNorm) x / √(mean(x²) + ε).
• Concept 02: The Transformer Decoder Block (SwiGLU & Feed-Forward)
  • The Everyday Problem: Attention routes information between tokens, but where does the model actually “think”, recall facts, and perform non-linear transformations?
  • Code & Math: Assembling the full Transformer Block: Pre-RMSNorm → Multi-Head Attention → Residual Add → Pre-RMSNorm → SwiGLU Feed-Forward → Residual Add.