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Deep learning creates representations that are expressed in terms of other, simpler representations

Deep Learning for AI

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Inventors have long dreamed of creating machines that think. Ancient Greek myths tell of intelligent objects, such as animated statues of human beings and tables that arrive full of food and drink when called . When programmable computers were first conceived,people wondered whether they might become intelligent, over a hundred years before one was built (Lovelace, 1842). Today, artificial intelligence (AI) is a thriving field with many practical applications and active research topics. We look to intelligent software to automate routine labor, make predictions in finance and diagnoses in medicine, and to support basic scientific research. This book is about deep learning, an approach toAI based on enabling computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined in terms of of its relation to simpler concepts.

Deep learning solves this central problem in representation learning by introducing representations that are expressed in terms of other, simpler representations. Deep learning allows the computer to build complex concepts out of simpler concepts. Fig. 1.1shows how a deep learning system can represent the concept of an image of a person by combining simpler concepts, such as corners and contours, which are in turn defined in terms of edges.

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It's turtles all the way down.

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