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Probabilistic deep learning : with Python, Keras, and TensorFlow Probability / Oliver Dürr, Beate Sick ; with Elvis Murina.

By: Contributor(s): Material type: TextPublication details: New York: Manning Publications Co. 2020Description: xviii, 274 pages : illustrations ; 24 cmISBN:
  • 9781617296079
Subject(s): DDC classification:
  • 006.31 23 D9651p
Contents:
Part 1, Basics of deep learning. Introduction to probabilistic deep learning ; Neural network architectures ; Principles of curve fitting -- Part 2, Maximum likelihood approaches for probabilistic DL models. Building loss functions with the likelihood approach ; Probabilistic deep learning models with TensorFlow Probability ; Probabilistic deep learning models in the wild -- Part 3, Bayesian approaches for probabilistic DL models. Bayesian learning ; Bayesian neural networks.
Summary: "A hands-on guide to the principles that support neural networks"--
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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 D9651p (Browse shelf(Opens below)) 2020 01 Not For Loan 029673
Books Library, Independent University, Bangladesh (IUB) Reference Stacks 006.31 D9651p (Browse shelf(Opens below)) 2020 02 Not For Loan 029674
Total holds: 0

Includes index.

"Exercises in Jupyter Notebooks"--Page 1 of cover.

Part 1, Basics of deep learning. Introduction to probabilistic deep learning ; Neural network architectures ; Principles of curve fitting -- Part 2, Maximum likelihood approaches for probabilistic DL models. Building loss functions with the likelihood approach ; Probabilistic deep learning models with TensorFlow Probability ; Probabilistic deep learning models in the wild -- Part 3, Bayesian approaches for probabilistic DL models. Bayesian learning ; Bayesian neural networks.

"A hands-on guide to the principles that support neural networks"--

"For experience machine learning developers"--Page 4 of cover.

School of engineering, Technology &Sciences Physical Science Reference Stacks

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