Chain Rule & Gradient Computation

Linear Algebra for Machine Learning · Axiom Academy

Matrix Chain Rule: Concrete Example Computing ∂L/∂W = ∂L/∂y · ∂y/∂W with numbers For a function composition y = f(g(x)), the derivative is: Worked Example: 2-Layer Network Backward Pass: Computing ∂L/∂W² Backward Pass: Computing ∂L/∂W¹ Notice the consistent formula across all layers: Unit 8: Linear Algebra in Neural Networks | Module 8: Matrix Chain Rule

This is the written version of the interactive lesson above. See the full Linear Algebra for Machine Learning course.