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210123 ||| eng |
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|a 9781638350408
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|a 163835040X
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|a 1617296074
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|a Q325.5
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|a Dürr, Oliver
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|a Probabilistic deep learning
|b with Python, Keras, and TensorFlow Probability
|c Oliver Dürr, Beate Sick ; with Elvis Murina
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260 |
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|a Shelter Island, New York
|b Manning Publications
|c 2020
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300 |
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|a 1 online resource
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|a Includes bibliographical references
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|a 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
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653 |
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|a Computer programming / http://id.loc.gov/authorities/subjects/sh85107310
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653 |
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|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
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653 |
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|a Programmation (Informatique)
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653 |
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|a Réseaux neuronaux (Informatique)
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653 |
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|a Neural networks (Computer science) / fast
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653 |
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|a Machine learning / fast
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|a computer programming / aat
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|a Apprentissage automatique
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|a Neural networks (Computer science) / http://id.loc.gov/authorities/subjects/sh90001937
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700 |
1 |
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|a Sick, Beate
|e author
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|a Murina, Elvis
|e author
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|a eng
|2 ISO 639-2
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|b OREILLY
|a O'Reilly
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500 |
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|a "Exercises in Jupyter Notebooks"--Cover
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776 |
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|z 1617296074
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|z 9781617296079
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|z 163835040X
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|z 9781638350408
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|u https://learning.oreilly.com/library/view/~/9781617296079/?ar
|x Verlag
|3 Volltext
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|a 500
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|a 006.31
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|a 331
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|a 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
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