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Deep learning

Deep learning (Loan 1 times)

Material type
단행본
Personal Author
Kelleher, John D., 1974- author.
Title Statement
Deep learning / John D. Kelleher.
Publication, Distribution, etc
Cambridge, Mass. :   MIT Press,   c2019.  
Physical Medium
x, 280 p. : ill. ; 18 cm.
Series Statement
The MIT press essential knowledge series
ISBN
9780262537551 (pbk. ; alk. paper)
요약
"Artificial Intelligence is a disruptive technology across business and society. There are three long-term trends driving this AI revolution: the emergence of Big Data, the creation of cheaper and more powerful computers, and development of better algorithms for processing an learning from data. Deep learning is the subfield of Artificial Intelligence that focuses on creating large neural network models that are capable of making accurate data driven decisions. Modern neural networks are the most powerful computational models we have for analyzing massive and complex datasets, and consequently deep learning is ideally suited to take advantage of the rapid growth in Big Data and computational power. In the last ten years, deep learning has become the fundamental technology in computer vision systems, speech recognition on mobile phones, information retrieval systems, machine translation, game AI, and self-driving cars. It is set to have a massive impact in healthcare, finance, and smart cities over the next years. This book is designed to give an accessible and concise, but also comprehensive, introduction to the field of Deep Learning. The book explains what deep learning is, how the field has developed, what deep learning can do, and also discusses how the field is likely to develop in the next 10 years. Along the way, the most important neural network architectures are described, including autoencoders, recurrent neural networks, long short-term memory networks, convolutional networks, and more recent developments such as Generative Adversarial Networks, transformer networks, and capsule networks. The book also covers the two more important algorithms for training a neural network, the gradient descent algorithm and Backpropagation"--
Bibliography, Etc. Note
Includes bibliographical references and index.
Subject Added Entry-Topical Term
Machine learning. Artificial intelligence.
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010 ▼a 2018059550
020 ▼a 9780262537551 (pbk. ; alk. paper)
035 ▼a (KERIS)REF000018880280
040 ▼a DLC ▼b eng ▼c DLC ▼e rda ▼d DLC ▼d 211009
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100 1 ▼a Kelleher, John D., ▼d 1974- ▼e author.
245 1 0 ▼a Deep learning / ▼c John D. Kelleher.
260 ▼a Cambridge, Mass. : ▼b MIT Press, ▼c c2019.
300 ▼a x, 280 p. : ▼b ill. ; ▼c 18 cm.
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a unmediated ▼b n ▼2 rdamedia
338 ▼a volume ▼b nc ▼2 rdacarrier
490 1 ▼a The MIT press essential knowledge series
504 ▼a Includes bibliographical references and index.
520 ▼a "Artificial Intelligence is a disruptive technology across business and society. There are three long-term trends driving this AI revolution: the emergence of Big Data, the creation of cheaper and more powerful computers, and development of better algorithms for processing an learning from data. Deep learning is the subfield of Artificial Intelligence that focuses on creating large neural network models that are capable of making accurate data driven decisions. Modern neural networks are the most powerful computational models we have for analyzing massive and complex datasets, and consequently deep learning is ideally suited to take advantage of the rapid growth in Big Data and computational power. In the last ten years, deep learning has become the fundamental technology in computer vision systems, speech recognition on mobile phones, information retrieval systems, machine translation, game AI, and self-driving cars. It is set to have a massive impact in healthcare, finance, and smart cities over the next years. This book is designed to give an accessible and concise, but also comprehensive, introduction to the field of Deep Learning. The book explains what deep learning is, how the field has developed, what deep learning can do, and also discusses how the field is likely to develop in the next 10 years. Along the way, the most important neural network architectures are described, including autoencoders, recurrent neural networks, long short-term memory networks, convolutional networks, and more recent developments such as Generative Adversarial Networks, transformer networks, and capsule networks. The book also covers the two more important algorithms for training a neural network, the gradient descent algorithm and Backpropagation"-- ▼c Provided by publisher.
650 0 ▼a Machine learning.
650 0 ▼a Artificial intelligence.
830 0 ▼a MIT press essential knowledge series.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.31 K29d Accession No. 121256750 Availability In loan Due Date 2021-07-07 Make a Reservation Available for Reserve R Service M

Contents information

Author Introduction

존 켈러허(지은이)

더블린공과대학교 컴퓨터과학부 교수이자 부속기관인 정보통신 및 엔터테인먼트 연구소 소장. 인공지능, 기계학습 분야에서 세계적으로 인정받는 전문가다. 더블린시립대학교, 유럽미디어연구소, 독일인공지능연구센터 등 여러 대학과 연구소에서 일했다. 지은 책으로 《딥러닝》 《데이터 예측을 위한 머신 러닝》(공저)이 있다.

Information Provided By: : Aladin

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