| Description |
1 online resource |
| Note |
"Exercises in Jupyter Notebooks"--Cover |
| 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 |
Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications. |
| Bibliography |
Includes bibliographical references. |
| Subject |
Machine learning.
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Neural networks (Computer science)
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Computer programming.
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Apprentissage automatique. |
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Réseaux neuronaux (Informatique) |
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Programmation (Informatique) |
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computer programming. |
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Machine learning |
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Neural networks (Computer science) |
| Added Author |
Sick, Beate, author.
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Murina, Elvis, author.
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| Other Form: |
Print version: Sick, Beate Probabilistic Deep Learning New York : Manning Publications Co. LLC,c2020 9781617296079 |
| ISBN |
9781638350408 (electronic book) |
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163835040X (electronic book) |
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1617296074 (paperback) |
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(paperback) |
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9781617296079 (paperback) |
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