How to Compute Chain-Rule Derivatives in Python with SymPy (in under 1.5 munutes)

Master the Chain Rule in calculus with Python using the SymPy library! This tutorial walks you through computing derivatives of composite functions step by step. Learn how to apply differentiation to nested functions like sin(x² + 5) and tan(sin(x² + 5)). Perfect for students, engineers, and data scientists working with symbolic computation!

✅ What You’ll Learn:
Understanding the Chain Rule for differentiation
How to differentiate composite functions in SymPy
Step-by-step breakdown of function composition
Practical applications for calculus, machine learning, and finance
📌 Code from the Tutorial:
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from sympy import sin, tan, Symbol, diff

x = Symbol('x')

g = x**2 + 5
f = sin(g)

diff(f, x) # Derivative using Chain Rule

f = tan(sin(g))
diff(f, x) # Nested Chain Rule Example
***

📊 Why This Matters?
The Chain Rule is one of the most essential concepts in calculus, widely used in machine learning, physics, and optimization problems. Mastering it in Python makes solving complex derivatives easier!