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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: Massachusetts : The MIT Press, 2017.Edition: First editionDescription: xiv, 785 pages : illustrations ; 24 cmISBN:
  • 9780262035613 (hardcover : alk. paper)
  • 0262035618 (hardcover : alk. paper)
Subject(s): DDC classification:
  • 006.3/1 G651d 2017 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
Books Books Premier University Faculty of Engineering Library 006.3/1 G651d 2017 1 Available 28602
Books Books Premier University Faculty of Engineering Library 006.3/1 G651d 2017 2 Available 28603
Books Books Premier University Faculty of Engineering Library 006.3/1 G651d 2017 3 Available 28604
Books Books Premier University Faculty of Engineering Library 006.3/1 G651d 2017 4 Available 28605
Books Books Premier University Faculty of Engineering Library 006.3/1 G651d 2017 5 Available 28606

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.

Computer Science & Engineering

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