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

By: Contributor(s): Material type: TextSeries: Adaptive computation and machine learningPublication details: England: The MIT press: 2018Edition: EnglandDescription: xxii, 775 pages : illustrations (some color) ; 24 cmISBN:
  • 9780262035613
  • 0262035618
Subject(s): DDC classification:
  • 006.31 23 G6461d
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 Vol info Copy number Status Barcode
Books Library, Independent University, Bangladesh (IUB) Reference Stacks 006.31 G6461d (Browse shelf(Opens below)) 2016 01 Not For Loan 029683
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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