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Probabilistic deep learning : with Python, Keras, and TensorFlow Probability /
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...
| Autores principales: | , , |
|---|---|
| Formato: | eBook |
| Idioma: | Inglés |
| Publicado: |
Shelter Island, New York :
Manning Publications,
[2020]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
| LEADER | 00000cam a2200000Ii 4500 | ||
|---|---|---|---|
| 001 | OR_on1228532447 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m d | ||
| 007 | cr |n||||||||| | ||
| 008 | 201211s2020 nyu o 000 0 eng d | ||
| 040 | |a VT2 |b eng |c VT2 |d TOH |d UKOBU |d OCLCF |d OCLCQ |d OCLCO |d OCLCQ | ||
| 020 | |a 9781638350408 |q (electronic book) | ||
| 020 | |a 163835040X |q (electronic book) | ||
| 020 | |a 1617296074 |q (paperback) | ||
| 020 | |z 9781617296079 |q (paperback) | ||
| 020 | |a 9781617296079 |q (paperback) | ||
| 029 | 1 | |a AU@ |b 000068857460 | |
| 029 | 1 | |a AU@ |b 000073555047 | |
| 035 | |a (OCoLC)1228532447 | ||
| 082 | 0 | 4 | |a 006.31 |q OCoLC |2 23/eng/20230216 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Dürr, Oliver |c (College teacher), |e author. | |
| 245 | 1 | 0 | |a Probabilistic deep learning : |b with Python, Keras, and TensorFlow Probability / |c Oliver Dürr, Beate Sick ; with Elvis Murina. |
| 264 | 1 | |a Shelter Island, New York : |b Manning Publications, |c [2020] | |
| 264 | 4 | |c ©2020 | |
| 300 | |a 1 online resource | ||
| 500 | |a "Exercises in Jupyter Notebooks"--Cover | ||
| 505 | 0 | |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. | |
| 520 | |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. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast | |
| 700 | 1 | |a Sick, Beate, |e author. | |
| 700 | 1 | |a Murina, Elvis, |e author. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781617296079/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 994 | |a 92 |b IZTAP | ||