Atlas · GenAI 2026
Transformer Architecture
Transformer architecture (self-attention, KV cache, GQA)
conceptPeak: 2020Neural ArchitecturesAI consensus: 3/3
Prerequisites
- hardDeep Learning
Self-attention, layer normalization, residual connections, softmax — all are DL building blocks assembled in the Transformer
- hardLinear Algebra
Q·Kᵀ/√d is a scaled dot product of matrices; multi-head attention is parallel matrix projections — Transformers ARE linear algebra in action
Recommended reference
Vaswani et al. (2017) 'Attention Is All You Need' — the foundational paper; pair with Jay Alammar (2018) 'The Illustrated Transformer' blog post for intuition
Notes from AI deep research
Anthropic Opus
Vaswani (2017) zmienil wszystko. Self-attention, KV cache, GQA — musisz to rozumiec matematycznie
OpenAI Deep Research
Dobór modeli i optymalizacje [OA#11]
Google Deep Think
Self-Attention, KV Cache, GQA [G#22]
Related skills
- → is subcategory of: Deep Learning(3/3)
- ← is subcategory of: Long-Context Modeling(2/3)
- ← is an instance of: Large Language Models (LLM)(1/3)
- ← is an instance of: Mixture of Experts(1/3)