Phase 0: Linear Algebra Foundations · ~60 minutes · Python · Julia
Linear Algebra Intuition
Every AI model is just matrix math wearing a fancy hat.
Hiring signal: Understanding of linear algebra intuition internals
What you will learn
- Implement vector and matrix operations (addition, dot product, matrix multiply) from scratch in Python
- Explain geometrically what the dot product, projection, and Gram-Schmidt process do
- Determine linear independence, rank, and basis of a set of vectors using row reduction
- Connect linear algebra concepts to their AI applications: embeddings, attention scores, and LoRA
Introduction
Type: Learn Languages: Python, Julia Prerequisites: Phase 0 Time: ~60 minutes
Learning Objectives
- Implement vector and matrix operations (addition, dot product, matrix multiply) from scratch in Python
- Explain geometrically what the dot product, projection, and Gram-Schmidt process do
- Determine linear independence, rank, and basis of a set of vectors using row reduction
- Connect linear algebra concepts to their AI applications: embeddings, attention scores, and LoRA
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, Connections, Exercises, Key Terms — 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