Kaikki kirjat 25 % alennuksella koodilla: BOOKS

  • check Yli 10 miljoonaa kirjaa
  • check Uutuuksia joka päivä
  • check Yli 1 miljoona asiakasta luottaa meihin
  • check Hyvät hinnat ja alennukset
  • check Toimitus koko Eurooppaan

Enhancing Surrogate-Based Optimization Through Parallelization - Frederik Rehbach

englanti
2024-05-31
215,97 € 287,96 €

-25% koodilla BOOKS

Toimittajalla varastossa

Toimitus 15-21 arkipäivässä

30 päivän palautusoikeus

This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible. Through in-depth analysis, the ... Täydellinen kuvaus

Saatat myös pitää

Kuvaus

This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible.
Through in-depth analysis, the need for parallel SBO solvers is emphasized, and it is demonstrated that they outperform model-free algorithms in scenarios with a low evaluation budget. The SBO approach helps practitioners save significant amounts of time and resources in hyperparameter tuning as well as other optimization projects. As a highlight, a novel framework for objectively comparing the efficiency of parallel SBO algorithms is introduced, enabling practitioners to evaluate and select the most effective approach for their specific use case.
Based on practical examples, decision support is delivered, detailing which parts of industrial optimization projects can be parallelized and how to prioritize which parts to parallelize first. By following the framework, practitioners can make informed decisions about how to allocate resources and optimize their models efficiently.

Lisätietoja

Kirjoittaja Frederik Rehbach
Julkaisija Springer Nature Switzerland
Julkaisuvuosi 2024
Kannen tyyppi Pehmeäkantinen
EAN 9783031306112
Kirjoita oma arvostelusi
Arvostelet: Enhancing Surrogate-Based Optimization Through Parallelization
Arvostelusi:

Goodreads-arvostelut

215,97 € 287,96 €