Enhancing deep learning with bayesian inference: create more powerful, robust deep learning systems with Bayesian deep learning in Python
Deep learning has an increasingly significant impact on our lives, from suggesting content to playing a key role in mission- and safety-critical applications. As the influence of these algorithms grows, so does the concern for the safety and robustness of the systems which rely on them. Simply put,...
Gespeichert in:
| Hauptverfasser: | , , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing
2023
|
| Online-Zugang: | DE-Aug4 DE-573 DE-898 DE-91 DE-706 Volltext |
| Zusammenfassung: | Deep learning has an increasingly significant impact on our lives, from suggesting content to playing a key role in mission- and safety-critical applications. As the influence of these algorithms grows, so does the concern for the safety and robustness of the systems which rely on them. Simply put, typical deep learning methods do not know when they don't know. The field of Bayesian Deep Learning contains a range of methods for approximate Bayesian inference with deep networks. These methods help to improve the robustness of deep learning systems as they tell us how confident they are in their predictions, allowing us to take more care in how we incorporate model predictions within our applications. Through this book, you will be introduced to the rapidly growing field of uncertainty-aware deep learning, developing an understanding of the importance of uncertainty estimation in robust machine learning systems. You will learn about a variety of popular Bayesian Deep Learning methods, and how to implement these through practical Python examples covering a range of application scenarios. By the end of the book, you will have a good understanding of Bayesian Deep Learning and its advantages, and you will be able to develop Bayesian Deep Learning models for safer, more robust deep learning systems. |
| Beschreibung: | 1 Online-Ressource (xx, 359 Seiten) |
| ISBN: | 9781803237251 |
Internformat
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| illustrated | Not Illustrated |
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| institution | BVB |
| isbn | 9781803237251 |
| language | English |
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| spelling | Benatan, Matthew Verfasser (DE-588)1245015052 aut Enhancing deep learning with bayesian inference create more powerful, robust deep learning systems with Bayesian deep learning in Python Matt Benatan, Jochem Gietema, Marian Schneider Birmingham Packt Publishing 2023 1 Online-Ressource (xx, 359 Seiten) txt rdacontent c rdamedia cr rdacarrier Deep learning has an increasingly significant impact on our lives, from suggesting content to playing a key role in mission- and safety-critical applications. As the influence of these algorithms grows, so does the concern for the safety and robustness of the systems which rely on them. Simply put, typical deep learning methods do not know when they don't know. The field of Bayesian Deep Learning contains a range of methods for approximate Bayesian inference with deep networks. These methods help to improve the robustness of deep learning systems as they tell us how confident they are in their predictions, allowing us to take more care in how we incorporate model predictions within our applications. Through this book, you will be introduced to the rapidly growing field of uncertainty-aware deep learning, developing an understanding of the importance of uncertainty estimation in robust machine learning systems. You will learn about a variety of popular Bayesian Deep Learning methods, and how to implement these through practical Python examples covering a range of application scenarios. By the end of the book, you will have a good understanding of Bayesian Deep Learning and its advantages, and you will be able to develop Bayesian Deep Learning models for safer, more robust deep learning systems. Gietema, Jochem Verfasser aut Schneider, Marian Verfasser aut Erscheint auch als Druck-Ausgabe 978-1-80324-688-8 https://portal.igpublish.com/iglibrary/search/PACKT0006798.html Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Benatan, Matthew Gietema, Jochem Schneider, Marian Enhancing deep learning with bayesian inference create more powerful, robust deep learning systems with Bayesian deep learning in Python |
| title | Enhancing deep learning with bayesian inference create more powerful, robust deep learning systems with Bayesian deep learning in Python |
| title_auth | Enhancing deep learning with bayesian inference create more powerful, robust deep learning systems with Bayesian deep learning in Python |
| title_exact_search | Enhancing deep learning with bayesian inference create more powerful, robust deep learning systems with Bayesian deep learning in Python |
| title_exact_search_txtP | Enhancing deep learning with bayesian inference create more powerful, robust deep learning systems with Bayesian deep learning in Python |
| title_full | Enhancing deep learning with bayesian inference create more powerful, robust deep learning systems with Bayesian deep learning in Python Matt Benatan, Jochem Gietema, Marian Schneider |
| title_fullStr | Enhancing deep learning with bayesian inference create more powerful, robust deep learning systems with Bayesian deep learning in Python Matt Benatan, Jochem Gietema, Marian Schneider |
| title_full_unstemmed | Enhancing deep learning with bayesian inference create more powerful, robust deep learning systems with Bayesian deep learning in Python Matt Benatan, Jochem Gietema, Marian Schneider |
| title_short | Enhancing deep learning with bayesian inference |
| title_sort | enhancing deep learning with bayesian inference create more powerful robust deep learning systems with bayesian deep learning in python |
| title_sub | create more powerful, robust deep learning systems with Bayesian deep learning in Python |
| url | https://portal.igpublish.com/iglibrary/search/PACKT0006798.html |
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