Back to Module 1.10: TransformersIn Progress
AI Lesson & Submodule
LayerNorm
Deconstruct mean-variance normalization across feature layers.
Why This Matters
Layer Normalization stabilizes learning by scaling values to standard distributions at each layer.
What You Will Learn
- •Calculate layer statistics
- •Compare Pre-LN vs Post-LN
- •Analyze scaling weights
Concepts Covered
Mean-variance normalizationsPre-LN vs Post-LN stabilityNormalization weights parameters
Mapped Foundation Project: Mini Transformer Block Explainer
Visual deconstruction of a standard decoder block, outlining normalizations, skip links, and output projections.
Architecture Preview
Block-by-block diagram tracking vector changes as inputs pass through decoder normalizations and linear mappings.
Input TokensLayer Norm Layer 1Multi-Head Attention Block
Tech Stack Planned
ReactTypeScriptFramer Motion
GitHub: Coming SoonLive Demo: Coming Soon
Coming SoonTechnical Interview Value
- ?Why does modern decoder architectures (like Llama/GPT) use Pre-LN instead of Post-LN?