Gradient Descent, LMS, and the Mathematics of Error Reduction | Chapter 3 of Why Machines Learn
Gradient Descent, LMS, and the Mathematics of Error Reduction | Chapter 3 of Why Machines Learn Chapter 3, “The Bottom of the Bowl,” from Why Machines Learn: The Elegant Math Behind Modern AI traces one of the most influential inventions in machine learning history: the Least Mean Squares (LMS) algorithm developed by Bernard Widrow and Ted Hoff. This chapter explores how the LMS rule allowed early artificial neurons to learn from errors through simple, iterative updates—setting the stage for modern optimization techniques like gradient descent and stochastic gradient descent. This post expands on the chapter’s narrative and explains the mathematical intuition behind how machines learn to minimize error. For a more guided walkthrough, be sure to watch the video summary above. Supporting Last Minute Lecture helps us continue creating clear, accessible study tools for students and lifelong learners. The Birth of the LMS Algorithm Widrow and Hoff developed the LMS algorithm w...