BitWithBite
AI & Machine Learning Worksheet
🤖 Linear Algebra in NumPy
SCORE___ / 20
NameClassDate
After this worksheet you can
Create arraysDot and matmulTranspose and inverse in codeVectorised speed🧠 Section A · Concept Check ● BEGINNER 4 × 1 = 4
1Dot product in NumPy:
2Matrix multiply operator:
3Transpose of array A:
4Inverse comes from:
🧮 Section B · Problem Solving ● INTERMEDIATE 2 + 3×3 = 11
5np.array([1,2,3]).shape is (,).
6np.zeros((2,3)) makes a 2x3 of .
7Code: dot of [1,2] and [3,4].
8Code: 2x2 identity times [[1,2],[3,4]].
9Why is NumPy 100x faster than loops?
🚀 Section C · Challenge ● CHALLENGE 5
10Check a matrix times its inverse.
💭 Reflection — the most useful thing I learned:
A ___/4 B ___/11 C ___/5 Total ___/20
Teacher's Signature
Parent's Signature
✂ answer key — fold or cut before handing out
1-A 2-A 3-A 4-A | 5 3 6 zeros 7 = np.dot([1,2],[3,4]) -> 11 8 = np.eye(2) @ A -> A 9 = C-optimised vectorised operations on whole arrays | 10 = A @ np.linalg.inv(A) is approx np.eye(n)