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Deep learning / Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

By: Contributor(s): Material type: TextTextSeries: Adaptive computation and machine learningPublication details: USA : MIT Press, 2016.Description: xxii, 775 p. : illustrations (some color) ; 24 cmISBN:
  • 9780262035613 (hardcover : alk. paper)
  • 0262035618 (hardcover : alk. paper)
Subject(s): DDC classification:
  • 006.31 GOO 23
LOC classification:
  • Q325.5 .G66 2016
Contents:
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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Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
Books Books Central Library, KUET On Display 006.31 GOO (Browse shelf(Opens below)) 01 Not For Loan 3010056476
Books Books Central Library, KUET On Display 006.31 GOO (Browse shelf(Opens below)) 02 Not For Loan 3010056477
Total holds: 0

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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