Introduction to deep learning

By:

Charniak, Eugene

Material type: Text

TextPublication details: Massachusetts: The MIT Press; c2018.Description: xii, 174pISBN:

9780262039512

Subject(s):

Computational learning theory

DDC classification:

006.31 CHA

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.

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Holdings
Item type	Current library	Call number	Status	Barcode
Book	International Management Institute New Delhi	006.31 CHA (Browse shelf(Opens below))	Available	22128

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Previous	006.3 YAN Artificial intelligence and games	006.301 TEG Life 3.0 : being human in the age of artificial intelligence	006.303 ENC Encyclopedia of artificial intelligence : the past, present, and future of AI	006.31 CHA Introduction to deep learning	006.31 HUD Machine learning for business : using amazon sagemaker and jupyter	006.31 JUN Machine learning : the basics	006.31 RHY Machine learning with R, the tidyverse, and mlr	Next

Includes index.

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.

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