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Hyperparameter Optimization in Machine Learning : Make Your Machine Learning and Deep Learning Models More Efficient
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Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient
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Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient (Paperback or Softback)
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Paperback or Softback. Condizione: New. Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient. Book.
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Hyperparameter Optimization in Machine Learning : Make Your Machine Learning and Deep Learning Models More Efficient
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient
Da: California Books, Miami, FL, U.S.A.
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Hyperparameter Optimization in Machine Learning
Prima edizione
Paperback. Condizione: New. 1st ed. Dive 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 you'll 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, you'll 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. What You Will LearnDiscover how changes in hyperparameters affect the model's performance.Apply different hyperparameter tuning algorithms to data science problemsWork with Bayesian optimization methods to create efficient machine learning and deep learning modelsDistribute hyperparameter optimization using a cluster of machinesApproach automated machine learning using hyperparameter optimizationWho This Book Is For Professionals and students working with machine learning.
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Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms (Paperback or Softback)
Lingua: Inglese
Editore: Packt Publishing 2/18/2021, 2021
ISBN 10: 1800567685 ISBN 13: 9781800567689
Da: BargainBookStores, Grand Rapids, MI, U.S.A.
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Paperback or Softback. Condizione: New. Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms. Book.
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Hyperparameter Optimization in Machine Learning
Da: Rarewaves.com USA, London, LONDO, Regno Unito
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Prima edizione
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Aggiungi al carrelloPaperback. Condizione: New. 1st ed. Dive 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 you'll 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, you'll 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. What You Will LearnDiscover how changes in hyperparameters affect the model's performance.Apply different hyperparameter tuning algorithms to data science problemsWork with Bayesian optimization methods to create efficient machine learning and deep learning modelsDistribute hyperparameter optimization using a cluster of machinesApproach automated machine learning using hyperparameter optimizationWho This Book Is For Professionals and students working with machine learning.
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Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
Da: California Books, Miami, FL, U.S.A.
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Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
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Prima edizione
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Hyperparameter Optimization in Machine Learning : Make Your Machine Learning and Deep Learning Models More Efficient
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
Da: Books Puddle, New York, NY, U.S.A.
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Condizione: New. pp. 312.
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Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient
Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. 166 pages. 9.00x6.00x0.50 inches. In Stock.
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Hyperparameter Optimization in Machine Learning : Make Your Machine Learning and Deep Learning Models More Efficient
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
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Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient
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Hyperparameter Optimization in Machine Learning : Make Your Machine Learning and Deep Learning Models More Efficient
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Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient
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Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
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Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
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Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient
Da: Books Puddle, New York, NY, U.S.A.
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Condizione: New. 1st ed. edition NO-PA16APR2015-KAP.
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Hyperparameter Optimization in Machine Learning
Da: Rarewaves USA United, OSWEGO, IL, U.S.A.
Valutazione del venditore 5 su 5 stelle
Prima edizione
Paperback. Condizione: New. 1st ed. Dive 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 you'll 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, you'll 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. What You Will LearnDiscover how changes in hyperparameters affect the model's performance.Apply different hyperparameter tuning algorithms to data science problemsWork with Bayesian optimization methods to create efficient machine learning and deep learning modelsDistribute hyperparameter optimization using a cluster of machinesApproach automated machine learning using hyperparameter optimizationWho This Book Is For Professionals and students working with machine learning.
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Hyperparameter Optimization in Machine Learning
Da: Rarewaves.com UK, London, Regno Unito
Valutazione del venditore 5 su 5 stelle
Prima edizione
EUR 48,50
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Aggiungi al carrelloPaperback. Condizione: New. 1st ed. Dive 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 you'll 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, you'll 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. What You Will LearnDiscover how changes in hyperparameters affect the model's performance.Apply different hyperparameter tuning algorithms to data science problemsWork with Bayesian optimization methods to create efficient machine learning and deep learning modelsDistribute hyperparameter optimization using a cluster of machinesApproach automated machine learning using hyperparameter optimizationWho This Book Is For Professionals and students working with machine learning.
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EUR 54,80
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Hyperparameter Optimization in Machine Learning | Make Your Machine Learning and Deep Learning Models More Efficient | Tanay Agrawal | Taschenbuch | xix | Englisch | 2020 | Apress | EAN 9781484265789 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Hyperparameter Optimization in Machine Learning (eng)
Da: Brook Bookstore On Demand, Napoli, NA, Italia
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EUR 46,56
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Aggiungi al carrelloCondizione: new. Questo è un articolo print on demand.
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Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
Da: Majestic Books, Hounslow, Regno Unito
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Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
Da: Biblios, Frankfurt am main, HESSE, Germania
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Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
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EUR 59,40
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Aggiungi al carrelloPaperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
