Backpropagation, Gradient Descent, and the Rise of Deep Learning | Chapter 10 of Why Machines Learn
Backpropagation, Gradient Descent, and the Rise of Deep Learning | Chapter 10 of Why Machines Learn Chapter 10, “The Algorithm That Silenced the Skeptics,” from Why Machines Learn: The Elegant Math Behind Modern AI recounts the breakthrough that resurrected neural networks and paved the way for modern deep learning: the backpropagation algorithm. Through compelling historical narrative and vivid mathematical explanation, Ananthaswamy traces how Geoffrey Hinton, David Rumelhart, and Ronald Williams helped transform neural networks from a struggling curiosity into a central pillar of artificial intelligence. This post expands on the chapter’s historical insights, mathematical foundations, and conceptual breakthroughs that made multi-layer neural networks finally learnable. For a step-by-step visual explanation of backpropagation, watch the full chapter summary above. Supporting Last Minute Lecture helps us continue providing in-depth, accessible analyses of essential machine lear...