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001432740 001__ 1432740
001432740 003__ OCoLC
001432740 005__ 20230309003531.0
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001432740 019__ $$a1225545505$$a1237465496$$a1238205701
001432740 020__ $$a9781484265796$$q(electronic bk.)
001432740 020__ $$a1484265793$$q(electronic bk.)
001432740 020__ $$z1484265785
001432740 020__ $$z9781484265789
001432740 0247_ $$a10.1007/978-1-4842-6579-6$$2doi
001432740 035__ $$aSP(OCoLC)1225068951
001432740 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dTOH$$dOCLCO$$dSFB$$dDCT$$dOCLCF$$dOCLCQ$$dOCLCO$$dCOM$$dOCLCQ
001432740 049__ $$aISEA
001432740 050_4 $$aQ325.5
001432740 08204 $$a006.3/1$$223
001432740 1001_ $$aAgrawal, Tanay,$$eauthor.
001432740 24510 $$aHyperparameter optimization in machine learning :$$bmake your machine learning and deep learning models more efficient /$$cTanay Agrawal.
001432740 264_1 $$a[Berkeley] :$$bApress,$$c[2021]
001432740 300__ $$a1 online resource
001432740 336__ $$atext$$btxt$$2rdacontent
001432740 337__ $$acomputer$$bc$$2rdamedia
001432740 338__ $$aonline resource$$bcr$$2rdacarrier
001432740 347__ $$atext file
001432740 347__ $$bPDF
001432740 500__ $$aIncludes index.
001432740 5050_ $$aChapter 1: Hyperparameters -- Chapter 2: Brute Force Hyperparameter Tuning -- Chapter 3: Distributed Hyperparameter Optimization -- Chapter 4: Sequential Model-Based Global Optimization and Its Hierarchical -- Chapter 5: Using HyperOpt -- Chapter 6: Hyperparameter Generating Condition Generative Adversarial Neural.
001432740 506__ $$aAccess limited to authorized users.
001432740 520__ $$aDive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods. This is a step-by-step guide to hyperparameter optimization, starting with what hyperparameters are and how they affect different aspects of machine learning models. It then goes through some basic (brute force) algorithms of hyperparameter optimization. Further, the author addresses the problem of time and memory constraints, using distributed optimization methods. Next youll discuss Bayesian optimization for hyperparameter search, which learns from its previous history. The book discusses different frameworks, such as Hyperopt and Optuna, which implements sequential model-based global optimization (SMBO) algorithms. During these discussions, youll focus on different aspects such as creation of search spaces and distributed optimization of these libraries. Hyperparameter Optimization in Machine Learning creates an understanding of how these algorithms work and how you can use them in real-life data science problems. The final chapter summaries the role of hyperparameter optimization in automated machine learning and ends with a tutorial to create your own AutoML script. Hyperparameter optimization is tedious task, so sit back and let these algorithms do your work. You will: Discover how changes in hyperparameters affect the models performance. Apply different hyperparameter tuning algorithms to data science problems Work with Bayesian optimization methods to create efficient machine learning and deep learning models Distribute hyperparameter optimization using a cluster of machines Approach automated machine learning using hyperparameter optimization.
001432740 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 10, 2021).
001432740 650_0 $$aMachine learning.
001432740 650_0 $$aMathematical optimization$$xComputer programs.
001432740 650_0 $$aOpen source software.
001432740 650_0 $$aComputer programming.
001432740 650_6 $$aApprentissage automatique.
001432740 650_6 $$aLogiciels libres.
001432740 650_6 $$aProgrammation (Informatique)
001432740 655_0 $$aElectronic books.
001432740 77608 $$iPrint version:$$aAgrawal, Tanay.$$tHyperparameter optimization in machine learning.$$d[Berkeley] : Apress, [2021]$$z1484265785$$z9781484265789$$w(OCoLC)1196840823
001432740 852__ $$bebk
001432740 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-6579-6$$zOnline Access$$91397441.1
001432740 909CO $$ooai:library.usi.edu:1432740$$pGLOBAL_SET
001432740 980__ $$aBIB
001432740 980__ $$aEBOOK
001432740 982__ $$aEbook
001432740 983__ $$aOnline
001432740 994__ $$a92$$bISE