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Introduction to Deep Learning
by Eugene Charniak

Summary: A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach. Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference. show more

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Product details Format Hardback | 192 pages Dimensions 178 x 229 x 21mm | 544.31g Publication date 29 Jan 2019 Publisher MIT Press Ltd Imprint MIT Press Publication City/Country Cambridge, United States Language English Illustrations note 75 b&w illus.; 150 Illustrations, unspecified ISBN10 0262039516 ISBN13 9780262039512 Bestsellers rank 92,722

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