Introduction to Deep Learning

Eugene Charniak (Brown University)

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English

Massachusetts Inst of Tec
29 January 2019

Mathematics & Sciences; Machine learning

Series: The MIT Press

"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."

By: Eugene Charniak (Brown University)
Imprint: Massachusetts Inst of Tec
Country of Publication: United States
Dimensions: Height: 229mm, Width: 178mm, Spine: 21mm
ISBN: 9780262039512
ISBN 10: 0262039516
Series: The MIT Press
Pages: 192
Publication Date: 29 January 2019
Recommended Age: From 18 to 99 years
Audience: College/higher education , Primary
Format: Paperback
Publisher's Status: Active

Eugene Charniak is Professor of Computer Science at Brown University. He is the author of Statistical Language Learning (MIT Press) and other books.