TRA CỨU
thông tin biểu ghi
ISBN 9780262035613
DDC 006.31
Tác giả CN Goodfellow, Ian
Nhan đề Deep learning / Ian Goodfellow, Yoshua Bengio, Aaron Courville
Thông tin xuất bản Cambridge, Massachusetts : The MIT Press, 2016
Mô tả vật lý xi, 775 p. : Illustrations ; 23 cm.
Tóm tắt 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.
Thuật ngữ chủ đề Machine learning
Thuật ngữ chủ đề Computers and IT
Thuật ngữ chủ đề Maschinelles Lernen
Khoa Khoa Công nghệ Thông tin
Tác giả(bs) CN Bengio, Yoshua
Tác giả(bs) CN Courville, Aaron
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100 |aGoodfellow, Ian
245 |aDeep learning / |cIan Goodfellow, Yoshua Bengio, Aaron Courville
260 |aCambridge, Massachusetts : |bThe MIT Press, |c2016
300 |axi, 775 p. : |bIllustrations ; |c23 cm.
520 |aDeep 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.
541 |aMua
650 |aMachine learning
650 |aComputers and IT
650 |aMaschinelles Lernen
690 |aKhoa Công nghệ Thông tin
700 |aBengio, Yoshua |eauthor
700 |aCourville, Aaron |eauthor
852|a300|bQ12_Kho Mượn_02|j(2): 068234-5
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