Probabilistic Deep Learning: with Python, Keras and TensorFlow Probability
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Shelter Island, NY
Manning Publications Company
[2020]
|
| Series: | Exercises in Jupyter Notebooks
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | xviii, 274 Seiten Illustrationen, Diagramme |
| ISBN: | 9781617296079 1617296074 |
Staff View
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brief contents 1 я Introduction to probabilistic deep learning 2 ■ Neural network architectures 3 ■ Principles of curve fitting rAJRT t 3 25 62 Maxim им likeeiiigox /ux-iioxches for 4 ■ Building loss functions with the likelihood approach 93 5 я Probabilistic deep learning models with TensorFlow Probability 128 6 ■ Probabilistic deep learning models in the wild Г3 157 BAV ESAAN AÌXNOXCHES FOR FROXAlíIEISTÍC 195 7 ■ Bayesian learning 8 197 я Bayesian neural networks 229
Probabilistic Deep Learning Dürr «Sick· Murina he world is a noisy and uncertain place. Probabilistic deep learning models capture that noise and uncertainty, pull ing it into real-world scenarios. Crucial for self-driving cars and scientific testing, these techniques help deep learning engineers assess the accuracy of their results, spot errors, and improve their understanding of how algorithms work. T Probabilistic Deep Learning is a hands-on guide to the princi ples 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-toapply code and uses popular frameworks to keep you focused on practical applications. What's Inside • Explore maximum likelihood and the statistical basis of deep learning • Discover probabilistic models that can indicate possible outcomes • Learn to use normalizing flows for modeling and generating complex distributions • Use Bayesian neural networks to access the uncertainty in the model For experienced machine learning developers. Oliver Dürr is a professor at the University of Applied Sciences in Konstanz, Germany. Beate Sick holds a chair for applied statistics at ZHAW and works as a researcher and lecturer at the University of Zurich. EİVİS Murina is a data scientist. |
| adam_txt |
brief contents 1 я Introduction to probabilistic deep learning 2 ■ Neural network architectures 3 ■ Principles of curve fitting rAJRT t 3 25 62 Maxim им likeeiiigox /ux-iioxches for 4 ■ Building loss functions with the likelihood approach 93 5 я Probabilistic deep learning models with TensorFlow Probability 128 6 ■ Probabilistic deep learning models in the wild Г3 157 BAV ESAAN AÌXNOXCHES FOR FROXAlíIEISTÍC 195 7 ■ Bayesian learning 8 197 я Bayesian neural networks 229
Probabilistic Deep Learning Dürr «Sick· Murina he world is a noisy and uncertain place. Probabilistic deep learning models capture that noise and uncertainty, pull ing it into real-world scenarios. Crucial for self-driving cars and scientific testing, these techniques help deep learning engineers assess the accuracy of their results, spot errors, and improve their understanding of how algorithms work. T Probabilistic Deep Learning is a hands-on guide to the princi ples 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-toapply code and uses popular frameworks to keep you focused on practical applications. What's Inside • Explore maximum likelihood and the statistical basis of deep learning • Discover probabilistic models that can indicate possible outcomes • Learn to use normalizing flows for modeling and generating complex distributions • Use Bayesian neural networks to access the uncertainty in the model For experienced machine learning developers. Oliver Dürr is a professor at the University of Applied Sciences in Konstanz, Germany. Beate Sick holds a chair for applied statistics at ZHAW and works as a researcher and lecturer at the University of Zurich. EİVİS Murina is a data scientist. |
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| author | Dürr, Oliver Sick, Beate Murina, Elvis |
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| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Book |
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| illustrated | Illustrated |
| index_date | 2024-07-03T15:57:11Z |
| indexdate | 2025-02-20T07:21:28Z |
| institution | BVB |
| isbn | 9781617296079 1617296074 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032409345 |
| oclc_num | 1232509908 |
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| physical | xviii, 274 Seiten Illustrationen, Diagramme |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Manning Publications Company |
| record_format | marc |
| series2 | Exercises in Jupyter Notebooks |
| spellingShingle | Dürr, Oliver Sick, Beate Murina, Elvis Probabilistic Deep Learning with Python, Keras and TensorFlow Probability Keras Framework, Informatik (DE-588)1160521077 gnd Deep Learning (DE-588)1135597375 gnd Python Programmiersprache (DE-588)4434275-5 gnd TensorFlow (DE-588)1153577011 gnd |
| subject_GND | (DE-588)1160521077 (DE-588)1135597375 (DE-588)4434275-5 (DE-588)1153577011 |
| title | Probabilistic Deep Learning with Python, Keras and TensorFlow Probability |
| title_auth | Probabilistic Deep Learning with Python, Keras and TensorFlow Probability |
| title_exact_search | Probabilistic Deep Learning with Python, Keras and TensorFlow Probability |
| title_exact_search_txtP | Probabilistic Deep Learning With Python, Keras and TensorFlow Probability |
| title_full | Probabilistic Deep Learning with Python, Keras and TensorFlow Probability Oliver Dürr, Beate Sick, with Elvis Murina |
| title_fullStr | Probabilistic Deep Learning with Python, Keras and TensorFlow Probability Oliver Dürr, Beate Sick, with Elvis Murina |
| title_full_unstemmed | Probabilistic Deep Learning with Python, Keras and TensorFlow Probability Oliver Dürr, Beate Sick, with Elvis Murina |
| title_short | Probabilistic Deep Learning |
| title_sort | probabilistic deep learning with python keras and tensorflow probability |
| title_sub | with Python, Keras and TensorFlow Probability |
| topic | Keras Framework, Informatik (DE-588)1160521077 gnd Deep Learning (DE-588)1135597375 gnd Python Programmiersprache (DE-588)4434275-5 gnd TensorFlow (DE-588)1153577011 gnd |
| topic_facet | Keras Framework, Informatik Deep Learning Python Programmiersprache TensorFlow |
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Indexes
THWS Schweinfurt Zentralbibliothek Lesesaal
| Call Number: |
2000 ST 302 D853 |
|---|---|
| Copy 1 | ausleihbar Available Place a Hold |
THWS Würzburg Zentralbibliothek Lesesaal
| Call Number: |
1000 ST 302 D853 |
|---|---|
| Copy 1 | ausleihbar Available Place a Hold |