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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: Cambridge: The MIT Press, 2016.Description: xxii, 775 pages : illustrations (some color) ; 24 cmISBN:
  • 9780262035613 (hardcover : alk. paper)
  • 0262035618 (hardcover : alk. paper)
Subject(s): DDC classification:
  • 006.31 G61D 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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Item type Current library Collection Call number Status Notes Date due Barcode
Books Books Central Library, IISER Bhopal General Section 006.31 G61D (Browse shelf(Opens below)) Checked out to Katta Naveen (20147) 16/10/2024 8746
Books Books Central Library, IISER Bhopal Reference Section Reference 006.31 G61D (Browse shelf(Opens below)) Not For Loan Reserve 8745
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006.30954 G35F Futures of artificial intelligence : 006.31 G319H Hands-on machine learning with Scikit-Learn and TensorFlow : 006.31 G549D Deep learning : 006.31 G61D Deep learning 006.31 M695M Machine Learning 006.31 M695M Machine Learning 006.31 M954M Machine learning :

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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