Module 3: Vector Backpropagation Engine

Welcome to Module 3: Vector Backpropagation Engine. In this module, we demystify the inner mechanics of automatic differentiation (Autograd)—the engine behind PyTorch, TensorFlow, and all modern deep learning.

Concepts in this Module

• Concept 01: Computational Graphs & Vector Chain Rule
  • The Everyday Problem: How does a change in an early motor parameter or weight ripple through multiple calculations to affect the final loss?
  • Code & Math: Directed Acyclic Graphs (DAG), forward pass values, backward pass local derivatives, and the multivariate Chain Rule dL/dx = dL/dy · dy/dx.
• Concept 02: Building an Autograd Engine in Pure Python
  • The Everyday Problem: How do PyTorch and neural network libraries calculate exact gradients for millions of parameters automatically without manual calculus?
  • Code & Math: The 30-line micro-autograd Value object, operator overloading (__add__, __mul__), and topological sort backward traversal.