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Ajouter au panierPaperback or Softback. Etat : New. Automated Deep Learning Using Neural Network Intelligence: Develop and Design Pytorch and Tensorflow Models Using Python 1.54. Book.
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ISBN 10 : 1484290925 ISBN 13 : 9781484290927
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Ajouter au panierEtat : New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
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Ajouter au panierPAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.
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Ajouter au panierEtat : Hervorragend. Zustand: Hervorragend | Seiten: 384 | Sprache: Englisch | Produktart: Bücher.
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ISBN 10 : 1484290925 ISBN 13 : 9781484290927
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Ajouter au panierEtat : Brand New. New.SoftCover International edition. Different ISBN and Cover image but contents are same as US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Ajouter au panierPaperback / softback. Etat : New. New copy - Usually dispatched within 4 working days. 751.
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Ajouter au panierPaperback. Etat : Brand New. 401 pages. 10.00x7.00x0.83 inches. In Stock.
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Ajouter au panierEtat : New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books!
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
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Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
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Ajouter au panierPaperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 751.
Edité par Springer, Berlin|Apress, 2022
ISBN 10 : 1484281489 ISBN 13 : 9781484281482
Langue: anglais
Vendeur : moluna, Greven, Allemagne
EUR 62,02
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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides technique.
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 65,35
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Ajouter au panierPaperback. Etat : Brand New. 401 pages. 10.00x7.00x0.83 inches. In Stock. This item is printed on demand.
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 69,54
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Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design are presented. The book teaches you how to construct a search space and launch an architecture search using the latest state-of-the-art exploration strategies: Efficient Neural Architecture Search (ENAS) and Differential Architectural Search (DARTS). You will learn how to automate the construction of a neural network architecture for a particular problem and dataset. The book focuses on model compression and feature engineering methods that are essential in automated deep learning. It also includes performance techniques that allow the creation of large-scale distributive training platforms using NNI. After reading this book, you will know how to use the full toolkit of automated deep learning methods. The techniques and practical examples presented in this book will allow you to bring your neural network routines to a higher level.What You Will LearnKnow the basic concepts of optimization tuners, search space, and trialsApply different hyper-parameter optimization algorithms to develop effective neural networksConstruct new deep learning models from scratchExecute the automated Neural Architecture Search to create state-of-the-art deep learning modelsCompress the model to eliminate unnecessary deep learning layersWho This Book Is ForIntermediate to advanced data scientists and machine learning engineers involved in deep learning and practical neural network development 384 pp. Englisch.
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 73,88
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierTaschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design are presented. The book teaches you how to construct a search space and launch an architecture search using the latest state-of-the-art exploration strategies: Efficient Neural Architecture Search (ENAS) and Differential Architectural Search (DARTS). You will learn how to automate the construction of a neural network architecture for a particular problem and dataset. The book focuses on model compression and feature engineering methods that are essential in automated deep learning. It also includes performance techniques that allow the creation of large-scale distributive training platforms using NNI. After reading this book, you will know how to use the full toolkit of automated deep learning methods. The techniques and practical examples presented in this book will allow you to bring your neural network routines to a higher level.What You Will LearnKnow the basic concepts of optimization tuners, search space, and trialsApply different hyper-parameter optimization algorithms to develop effective neural networksConstruct new deep learning models from scratchExecute the automated Neural Architecture Search to create state-of-the-art deep learning modelsCompress the model to eliminate unnecessary deep learning layersWho This Book Is ForIntermediate to advanced data scientists and machine learning engineers involved in deep learning and practical neural network development.
Vendeur : Majestic Books, Hounslow, Royaume-Uni
EUR 86,42
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Ajouter au panierEtat : New. Print on Demand.
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
EUR 89,08
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Ajouter au panierEtat : New. PRINT ON DEMAND.