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Introduction Deep Learning Using by Beysolow (17 results)
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Paperback. Condition: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
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Condition: As New. Unread book in perfect condition.
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Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 60.59
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In English.
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Introduction to Deep Learning Using R : A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Introduction to Deep Learning Using R : A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Paperback. Condition: Brand New. 227 pages. 9.00x6.00x0.75 inches. In Stock.
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Taschenbuch. Condition: Neu. Introduction to Deep Learning Using R | A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R | Taweh Beysolow Ii | Taschenbuch | xix | Englisch | 2017 | Apress | EAN 9781484227336 | 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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Paperback. Condition: New. New. book.
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Condition: new. Questo è un articolo print on demand.
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Introduction to Deep Learning Using R
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Understand deep learning, the nuances of its different models, and where these models can be applied.The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools.What You'll LearnUnderstand the intuition and mathematics that power deep learning modelsUtilize various algorithms using the R programming language and its packagesUse best practices for experimental design and variable selectionPractice the methodology to approach and effectively solve problems as a data scientistEvaluate the effectiveness of algorithmic solutions and enhance their predictive power Who This Book Is For Students, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful. 252 pp. Englisch.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The code in this book utilizes R studio and its packages, all of which are open source, to make the learning process as simple as possibleEach chapter builds upon the knowledge of the preceding chapter.
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Introduction to Deep Learning Using R
Language: English
Published by Apress, Apress Jul 2017, 2017
ISBN 10: 1484227336 ISBN 13: 9781484227336
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Understand deep learning, the nuances of its different models, and where these models can be applied.The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools.What You'll LearnUnderstand the intuition and mathematics that power deep learning modelsUtilize various algorithms using the R programming language and its packagesUse best practices for experimental design and variable selectionPractice the methodology to approach and effectively solve problems as a data scientistEvaluate the effectiveness of algorithmic solutions and enhance their predictive powerSpringer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 252 pp. Englisch.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Understand deep learning, the nuances of its different models, and where these models can be applied.The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools.What You'll LearnUnderstand the intuition and mathematics that power deep learning modelsUtilize various algorithms using the R programming language and its packagesUse best practices for experimental design and variable selectionPractice the methodology to approach and effectively solve problems as a data scientistEvaluate the effectiveness of algorithmic solutions and enhance their predictive power Who This Book Is For Students, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.
