Deep learning / Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
Material type:
TextSeries: Adaptive computation and machine learningPublisher: Cambridge, Massachusetts : The MIT Press, [2016]Copyright date: ©2016Description: xxii, 775 pages : illustrations (some color); 24 cmContent type: - text
- unmediated
- volume
- 9780262035613
- 006.3/1 23
- Q325.5 .G66 2016
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UPM Female Campus Library FR | Computer and Cyber Sciences | 006.31 GID (Browse shelf(Opens below)) | C.1 | Available | UPM0000005265 | ||
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UPM Female Campus Library FR | Computer and Cyber Sciences | 006.31 GID (Browse shelf(Opens below)) | C.2 | Available | UPM0000005266 | ||
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UPM Male Campus Library FR | Computer and Cyber Sciences | 006.31 GID (Browse shelf(Opens below)) | C.3 | Available | UPM0000005267 | ||
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UPM Male Campus Library FR | Computer and Cyber Sciences | 006.31 GID (Browse shelf(Opens below)) | C.4 | Available | UPM0000005268 | ||
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UPM Male Campus Library FR | Computer and Cyber Sciences | 006.31 GID (Browse shelf(Opens below)) | C.5 | Available | UPM0000005269 |
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| 006.31 DLD Deep learning : methods and applications | 006.31 DLD Deep learning : methods and applications | 006.31 GAH Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems | 006.31 GID Deep learning | 006.31 GID Deep learning | 006.31 GID Deep learning | 006.31 HCD Designing machine learning systems : an iterative process for production-ready applications |
Includes bibliographical references (pages 711-766) and index.
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.
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