Back to AI DashboardModule 4: Transformer Architecture
AI Engineer Track
Module 4: Transformer Architecture
Deep dive into Attention Is All You Need. Study positional encoding, multi-head attention, and decoder layers.
Syllabus Modules
Attention OriginsComing Soon
Compare sequence modeling constraints in sequential vs parallel inputs.
Total Lessons: 0Explore Module
Encoder-DecoderComing Soon
Study joint cross-attention mapping systems in classical transformers.
Total Lessons: 0Explore Module
Scaled Dot-Product MathComing Soon
Derive Q, K, and V matrix dot product scaling constraints.
Total Lessons: 0Explore Module
Multi-Head AttentionComing Soon
Study parallel attention splitting dimensions routing.
Total Lessons: 0Explore Module
Positional EncodingComing Soon
Verify sine and cosine coordinate matrices positional additions.
Total Lessons: 0Explore Module
Feed ForwardComing Soon
Learn MLP sublayers and activation bounds inside blocks.
Total Lessons: 0Explore Module
Layer NormalizationComing Soon
Compare pre-LN and post-LN gradients training stability configurations.
Total Lessons: 0Explore Module
Track Progress
0 / 7Projects Verified
Learning Outcomes
- Compute scaled dot-product attention manually
- Implement sine/cosine positional embedding matrices
- Understand layer norm vs batch norm scaling constraints
Interview Defense
- Explain why self-attention runs in O(N^2) space complexity
- Describe the role of residual connections in deep transformers