Indira Gandhi National Tribal University, Amarkantak

Prof. Ram Dayal Munda Central Library

Online Public Access Catalogue

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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 pages : illustrations (some color)ISBN:
  • 9780262035613
Subject(s): DDC classification:
  • 006.31 GOO
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 Date due Barcode
Books Books Prof. Ram Dayal Munda Central Library General Stacks Computer Science 006.31 GOO (Browse shelf(Opens below)) Available 69049
Books Books Prof. Ram Dayal Munda Central Library Computer Science 006.31 GOO (Browse shelf(Opens below)) Available 67519

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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