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1
Computer architecture : a quantitative approach / John L Hennessy; David A Patterson
Cambridge, MA : Morgan Kaufmann Publishers, 2019
1527 tr. : illustrations ; 24 cm.
Ký hiệu phân loại (DDC): 004.22
Computer Architecture: A Quantitative Approach, Sixth Edition has been considered essential reading by instructors, students and practitioners of computer design for over 20 years. The sixth edition of this classic textbook is fully revised with the latest developments in processor and system architecture. It now features examples from the RISC-V (RISC Five) instruction set architecture, a modern RISC instruction set developed and designed to be a free and openly adoptable standard. It also includes a new chapter on domain-specific architectures and an updated chapter on warehouse-scale computing that features the first public information on Google's newest WSC. True to its original mission of demystifying computer architecture, this edition continues the longstanding tradition of focusing on areas where the most exciting computing innovation is happening, while always keeping an emphasis on good engineering design.
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2
Computer networking : a top-down approach / James F Kurose; Keith W Ross
Boston, Mass. : Pearson, 2013
tr. : illustrations
Ký hiệu phân loại (DDC): 004.6
Building on the successful top-down approach of previous editions, 'Computer Networking' continues with an early emphasis on application-layer paradigms and application programming interfaces, encouraging a hands-on experience with protocols and networking concepts.
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3
Computer vision : a modern approach / David Forsyth; Jean Ponce
Harlow : Pearson Education, 2012
30 p. : illustrations ; cm.
Ký hiệu phân loại (DDC): 006.37
Appropriate for upper-division undergraduate and graduate level courses in computer vision found in departments of computer science, computer engineering and electrical engineering, this book offers a treatment of modern computer vision methods.
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4
Computer vision : models, learning, and inference / Simon J D Prince
New York : Cambridge University Press, 2012
18 p.
Ký hiệu phân loại (DDC): 006.37
"This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. [bullet] Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry [bullet] A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking [bullet] More than 70 algorithms are described in sufficient detail to implement [bullet] More than 350 full-color illustrations amplify the text [bullet] The treatment is self-contained, including all of the background mathematics [bullet] Additional resources at www.computervisionmodels.com"
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5
Deep learning / Ian Goodfellow, Yoshua Bengio, Aaron Courville
Cambridge, Massachusetts : The MIT Press, 2016
xi, 775 p. : Illustrations ; 23 cm.
Ký hiệu phân loại (DDC): 006.31
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.
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