Restricted Boltzmann Machines, Deep Belief Networks, and the Mathematics of Artificial Dreaming | Chapter 12 of Why Machines Learn
Restricted Boltzmann Machines, Deep Belief Networks, and the Mathematics of Artificial Dreaming | Chapter 12 of Why Machines Learn Chapter 12, “Machines That Dream,” from Why Machines Learn: The Elegant Math Behind Modern AI explores one of the most imaginative and mathematically rich frontiers of modern AI: generative models and their capacity to “dream.” Drawing on physics, neuroscience, and machine learning, Anil Ananthaswamy explains how restricted Boltzmann machines (RBMs) and deep belief networks (DBNs) learn probability distributions over data and generate new samples from their internal representations. This chapter reveals how machines can hallucinate, reconstruct, and imagine—echoing the way biological brains dream. To explore how RBMs and DBNs generate patterns, be sure to watch the embedded video summary above. Supporting Last Minute Lecture helps us create accessible, academically grounded walkthroughs of advanced AI concepts. Energy-Based Models: The Foundatio...