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Bayesian optimization : theory and practice using Python /
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 approach...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Apress,
2023.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
| LEADER | 00000cam a22000007i 4500 | ||
|---|---|---|---|
| 001 | OR_on1374035348 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
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| 008 | 230328s2023 nyua o 001 0 eng d | ||
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| 020 | |a 9781484290637 |q electronic book | ||
| 020 | |a 1484290631 |q electronic book | ||
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| 020 | |z 9781484290620 | ||
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| 035 | |a (OCoLC)1374035348 | ||
| 037 | |a 9781484290637 |b O'Reilly Media | ||
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| 049 | |a UAMI | ||
| 100 | 1 | |a Liu, Peng, |e author. | |
| 245 | 1 | 0 | |a Bayesian optimization : |b theory and practice using Python / |c Peng Liu. |
| 264 | 1 | |a New York, NY : |b Apress, |c 2023. | |
| 300 | |a 1 online resource (xv, 234 pages) : |b illustrations (black and white, and colour). | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 500 | |a Includes index. | ||
| 520 | |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. | ||
| 505 | 0 | |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. | |
| 588 | 0 | |a Print version record. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Bayesian statistical decision theory |x Data processing. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 6 | |a Théorie de la décision bayésienne |x Informatique. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Optimisation mathématique. | |
| 650 | 7 | |a Bayesian statistical decision theory |x Data processing |2 fast | |
| 650 | 7 | |a Mathematical optimization |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 776 | 0 | 8 | |c Original |z 1484290623 |z 9781484290620 |w (OCoLC)1349562792 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781484290637/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH41349392 | ||
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL7219539 | ||
| 938 | |a YBP Library Services |b YANK |n 304761902 | ||
| 994 | |a 92 |b IZTAP | ||