.png&w=1536&q=75)
Free Lesson
Build The Self-Attention in PyTorch From Scratch
90 min
May 2, 2025 12:30 PM
Virtual (Zoom)
In this video
What you'll learn
Master the core of the Transformer Architecture
You’ll translate the mathematical formula into PyTorch code, so you can debug the very heart of every LLM.
Build a Fully Functional Multi‑Head Self‑Attention Module
Parallelize multiple attention heads on Q/K/V tensors, concat head outputs, and apply final linear projection.
Validate Attention on Toy Inputs
Test your module with sample token embeddings, verify output shapes, and inspect attention score matrices.
Why this topic matters
Building self‑attention from scratch bridges theory and practice. You’ll master the core LLM mechanism, customizing, debugging, and optimizing attention layers, which hiring managers prize for production AI. After this lesson, you’ll own runnable PyTorch code and the confidence to tackle full Transformer blocks and advanced LLM workflows.
You'll learn from
Damien Benveniste
Former Meta ML Tech Lead, CEO @ AiEdge
Previously at
