| ISBN |
0262035618 (hardcover : alk. paper)
|
| ISBN13桁 |
9780262035613 (hardcover : alk. paper)
|
| テキストの言語 |
英語
|
| 分類：NDC10版 |
007.13
|
| 個人著者標目 |
Goodfellow, Ian.
|
| 本タイトル |
Deep learning /
|
| 著者名 |
Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
|
| 出版地・頒布地 |
Cambridge, Massachusetts :
|
| 出版者・頒布者名 |
The MIT Press,
|
| 出版年・頒布年 |
c2016.
|
| 数量 |
xxii, 775 pages :
|
| 他の形態的事項 |
illustrations (some color) ;
|
| 大きさ |
24 cm.
|
| 書誌注記 |
Includes bibliographical references (pages [711]-766) and index.
|
| 内容注記 |
Introduction -- APPLIED MATH AND MACHINE LEARNING BASICS -- Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- DEEP NETWORKS: MODERN PRACTICES -- Deep feedforward networks -- Regularization for deep learning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- DEEP LEARNING RESEARCH -- Linear factor models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Monte Carlo methods -- Confronting the partition function -- Approximate inference -- Deep generative models.
|
| 要約、抄録、注釈等 |
"Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and video games. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors"--Page 4 of cover.
|
| 著者標目 |
Bengio, Yoshua.
Courville, Aaron.
|
| 統一タイトル(シリーズ副出標目) |
Adaptive computation and machine learning.
|
| シリーズ名・巻次 |
Adaptive computation and machine learning |
| 一般件名 |
Machine learning.
Machine learning. |
| 資料情報1 |
『Deep learning /』（Adaptive computation and machine learning） Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The MIT Press, c2016.
（所蔵館：中央
請求記号：F/007.1/G65/D
資料コード：7109496180）
|
| URL |
https://catalog.library.metro.tokyo.lg.jp/winj/opac/switch-detail.do?lang=ja&bibid=1352027743 |
