Five rules make differentiation mechanical: power, product, quotient, chain, and implicit. Once you know them, you never need the limit definition again for standard functions.
Power rule
d/dx [xⁿ] = n xⁿ⁻¹. This handles every polynomial. Combined with linearity (derivative of a sum is the sum of derivatives, constants pull out), it covers all polynomials instantly.
d/dx [f(x) g(x)] = f'(x) g(x) + f(x) g'(x). The derivative of a product is not the product of the derivatives. Both functions contribute to the rate of change.
d/dx [f(g(x))] = f'(g(x)) · g'(x). The derivative of a composition is the product of the derivatives, each evaluated at the right point. This is the most used rule in practice.
When y is defined implicitly by an equation like x² + y² = 1, differentiate both sides with respect to x, treating y as a function of x. Then solve for dy/dx. For the circle: 2x + 2y(dy/dx) = 0, so dy/dx = -x/y.
Scheme
; Implicit differentiation: x^2 + y^2 = 1 (unit circle); dy/dx = -x/y; At the point (3/5, 4/5) on the circle:
(define x 0.6) ; 3/5
(define y 0.8) ; 4/5
(display "x^2 + y^2 = ") (display (+ (* x x) (* y y))) (newline) ; 1.0
(display "dy/dx = -x/y = ") (display (/ (- x) y)) (newline) ; -0.75; Verify numerically: y = sqrt(1 - x^2)
(define (y-explicit x) (sqrt (- 1 (* x x))))
(define h 0.00001)
(define numerical-slope
(/ (- (y-explicit (+ x h)) (y-explicit (- x h))) (* 2 h)))
(display "Numerical dy/dx = ") (display (/ (round (* numerical-slope 10000)) 10000))
Python
importmath# Implicit differentiation: x^2 + y^2 = 1# dy/dx = -x/y
x, y = 0.6, 0.8# point on unit circleprint("x^2 + y^2 =", x**2 + y**2)
print("dy/dx = -x/y =", -x/y)
# Verify numerically: y = sqrt(1 - x^2)
y_explicit = lambda x: math.sqrt(1 - x**2)
h = 1e-5
numerical = (y_explicit(x+h) - y_explicit(x-h)) / (2*h)
print("Numerical dy/dx = {:.4f}".format(numerical))