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AI Syllabus Module
Module 1.10: Transformers
Decode transformer architecture blocks. Study layer normalizations and feed-forward neural layers.
Lessons & Submodules
Transformer Block Overview
Analyze the sequence flow of a decoder-only block.
Attention + Residual Connection
Understand how skip connections preserve gradient flows.
LayerNorm
Deconstruct mean-variance normalization across feature layers.
Feed Forward Network
Explore multi-layer perceptrons and non-linear activations inside blocks.
Decoder-only Architecture
Analyze GPT/Llama models and causal decoding loops.
Transformer Failure Modes
Diagnose gradient explosion, representation collapse, and VRAM leaks.
Transformer Interview Guide
Prepare for engineering interviews focused on transformer architectures.
Key Skills
- •Detail the execution paths of a standard decoder block
- •Describe the role of residual skip connections in preventing gradient vanishing
Interview Value
- Why did Multi-Head Attention replace single-head attention in production LLM backbones?