10.1410 min read
Differentiation Rules
Rather than computing every derivative from the limit definition, we use differentiation rules that package common patterns. The essential rules: constant ; power ; sum ; product ; chain rule .
The chain rule is the most important: if and , then . In Leibniz notation, derivatives multiply like fractions. This rule will be central in Chapter 14 when we extend it to multivariate functions.
Special derivatives: , , (for ).
Formal View
Theorem 10.1 — Differentiation Rules
For differentiable :\n- Power: \n- Sum: \n- Product: \n- Quotient: \n- Chain:
The chain rule in Leibniz form: .
Interactive Visualization
Interactive Line Explorer
Why This Matters
Differentiation rules make computing derivatives fast — essential for implementing gradient descent and backpropagation.
- Backpropagation: the chain rule applied recursively to a composition of functions (the neural network).
- Automatic differentiation: modern ML frameworks (PyTorch, JAX) implement these rules algorithmically.
- Sensitivity analysis: product and chain rules let you trace how changes propagate through complex models.
Quiz
Question 1
What is the derivative of ?
Question 2
The chain rule says: if , then
Common Mistakes
- Applying the power rule as (forgetting the factor of ) — it should be .
- Chain rule error: computing instead of — must evaluate the outer derivative at the inner function.