Phase 0: Attention Mechanisms · ~90 minutes · Python
Self-Attention from Scratch
Attention is a lookup table where every word asks "who matters to me?" - and learns the answer.
Hiring signal: Understanding of self-attention from scratch internals
What you will learn
- Implement scaled dot-product self-attention from scratch using only NumPy, including query/key/value projections and the softmax-weighted sum
- Build a multi-head attention layer that splits heads, computes parallel attention, and concatenates results
- Trace how the attention matrix captures token relationships and explain why scaling by sqrt(d_k) prevents softmax saturation
- Apply causal masking to convert bidirectional attention into autoregressive (decoder-style) attention
Introduction
Type: Build Languages: Python Prerequisites: Phase 3 (Deep Learning Core), Phase 5 Lesson 10 (Sequence-to-Sequence) Time: ~90 minutes
Learning Objectives
- Implement scaled dot-product self-attention from scratch using only NumPy, including query/key/value projections and the softmax-weighted sum
- Build a multi-head attention layer that splits heads, computes parallel attention, and concatenates results
- Trace how the attention matrix captures token relationships and explain why scaling by sqrt(d_k) prevents softmax saturation
- Apply causal masking to convert bidirectional attention into autoregressive (decoder-style) attention
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.
Create a free account to unlock Phase 0 and Phase 1 of every course — no credit card.
Browse all courses · View pricing · DeVenture Academy