Topic 3: Assembling the Transformer Architecture

Topic: Topic 3: Assembling the Transformer Architecture

Content adapted from Attention Is All You Need by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin.Original Source

Topic 3 Assembling The Transformer Architecture Introduction

Learn to stabilize ultra-deep Transformer stacks using residual connections and LayerNorm. Master causal masking to enforce auto-regression and prevent leakage.

Transformer Optimization: Residuals & Layer Normalization

Master the 'Add & Norm' paradigm. Learn how residual connections and Layer Normalization stabilize gradients and feature distributions in deep Transformers.

Causal Masking: Solving Information Leakage in Transformers

Learn how causal masking prevents information leakage in Transformer decoders, enabling parallel training while maintaining auto-regressive integrity.

Transformer Macro-Architecture: Stacks and Sub-layers

Master the Transformer's structure. Explore N=6 layer stacks, causal masking, residual connections, FFN logic, and weight-tying strategies.

Topic 3 Assembling The Transformer Architecture Guided Practice

Analyze Jacobian gradient flow, stabilize signals with LayerNorm, and master causal masking to ensure architectural stability and efficient training.