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|a 1484290631
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| 050 |
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4 |
|a QA279.5
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| 100 |
1 |
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|a Liu, Peng
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| 245 |
0 |
0 |
|a Bayesian optimization
|b theory and practice using Python
|c Peng Liu
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| 260 |
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|a New York, NY
|b Apress
|c 2023
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| 300 |
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|a xv, 234 pages
|b illustrations (black and white, and colour)
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| 505 |
0 |
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|a Chapter 1: Bayesian Optimization Overview -- Chapter 2: Gaussian Process -- Chapter 3: Bayesian Decision Theory and Expected Improvement -- Chapter 4 : Gaussian Process Regression with GPyTorch -- Chapter 5: Monte Carlo Acquisition Function with Sobol Sequences and Random Restart -- Chapter 6 : Knowledge Gradient: Nested Optimization versus One-shot Learning -- Chapter 7 : Case Study: Tuning CNN Learning Rate with BoTorch
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| 653 |
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|a Optimisation mathématique
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| 653 |
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|a Bayesian statistical decision theory / Data processing
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| 653 |
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|a Mathematical optimization / http://id.loc.gov/authorities/subjects/sh85082127
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| 653 |
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|a Python (Computer program language) / fast
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| 653 |
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|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
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| 653 |
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|a Mathematical optimization / fast
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| 653 |
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|a Théorie de la décision bayésienne / Informatique
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| 653 |
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|a Python (Langage de programmation)
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| 653 |
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|a Bayesian statistical decision theory / Data processing / fast
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Includes index
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| 024 |
8 |
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|a 10.1007/978-1-4842-9063-7
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| 776 |
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|z 9781484290620
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| 776 |
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|z 1484290623
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| 776 |
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|z 9781484290637
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| 776 |
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|z 1484290631
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| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9781484290637/?ar
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 519.5/42
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| 520 |
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|a This book covers the essential theory and implementation of popular Bayesian optimization techniques in an intuitive and well-illustrated manner. The techniques covered in this book will enable you to better tune the hyperparemeters of your machine learning models and learn sample-efficient approaches to global optimization
|