Amazon cover image
Image from Amazon.com

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"--
Tags from this library: No tags from this library for this title. Log in to add tags.

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

law

There are no comments on this title.

to post a comment.