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Añadir al carritopaperback. Condición: New. Marques, Anthony; Bone, J. Ilustrador. New item in gift quality condition. 99% of orders arrive in 4-10 days. Discounted shipping on multiple books.
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EUR 62,06
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Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2019
ISBN 10: 1138499986 ISBN 13: 9781138499980
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 64,41
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Añadir al carritoPaperback. Condición: new. Paperback. "This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist."- Professor Charles Bouveyron, INRIA Chair in Data Science, Universite Cote dAzur, Nice, FranceJulia, an open-source programming language, was created to be as easy to use as languages such as R and Python while also as fast as C and Fortran. An accessible, intuitive, and highly efficient base language with speed that exceeds R and Python, makes Julia a formidable language for data science. Using well known data science methods that will motivate the reader, Data Science with Julia will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work.Features: Covers the core components of Julia as well as packages relevant to the input, manipulation and representation of data. Discusses several important topics in data science including supervised and unsupervised learning. Reviews data visualization using the Gadfly package, which was designed to emulate the very popular ggplot2 package in R. Readers will learn how to make many common plots and how to visualize model results. Presents how to optimize Julia code for performance. Will be an ideal source for people who already know R and want to learn how to use Julia (though no previous knowledge of R or any other programming language is required). The advantages of Julia for data science cannot be understated. Besides speed and ease of use, there are already over 1,900 packages available and Julia can interface (either directly or through packages) with libraries written in R, Python, Matlab, C, C++ or Fortran. The book is for senior undergraduates, beginning graduate students, or practicing data scientists who want to learn how to use Julia for data science."This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist."Professor Charles BouveyronINRIA Chair in Data ScienceUniversite Cote dAzur, Nice, France There is a dearth of resources for data scientists, statisticians, etc., wishing to learn about Julia. Using well known data science methods, this book will both motivate the reader and assuage any unease. The book will get readers up to speed on key features of the Julia language and illustrate some of its advantages for data science work. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Añadir al carritoCondición: New. pp. 240.
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Añadir al carritoPaperback. Condición: Brand New. 236 pages. 9.21x6.14x0.55 inches. In Stock.
Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2019
ISBN 10: 1138499986 ISBN 13: 9781138499980
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 88,71
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Añadir al carritoPaperback. Condición: new. Paperback. "This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist."- Professor Charles Bouveyron, INRIA Chair in Data Science, Universite Cote dAzur, Nice, FranceJulia, an open-source programming language, was created to be as easy to use as languages such as R and Python while also as fast as C and Fortran. An accessible, intuitive, and highly efficient base language with speed that exceeds R and Python, makes Julia a formidable language for data science. Using well known data science methods that will motivate the reader, Data Science with Julia will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work.Features: Covers the core components of Julia as well as packages relevant to the input, manipulation and representation of data. Discusses several important topics in data science including supervised and unsupervised learning. Reviews data visualization using the Gadfly package, which was designed to emulate the very popular ggplot2 package in R. Readers will learn how to make many common plots and how to visualize model results. Presents how to optimize Julia code for performance. Will be an ideal source for people who already know R and want to learn how to use Julia (though no previous knowledge of R or any other programming language is required). The advantages of Julia for data science cannot be understated. Besides speed and ease of use, there are already over 1,900 packages available and Julia can interface (either directly or through packages) with libraries written in R, Python, Matlab, C, C++ or Fortran. The book is for senior undergraduates, beginning graduate students, or practicing data scientists who want to learn how to use Julia for data science."This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist."Professor Charles BouveyronINRIA Chair in Data ScienceUniversite Cote dAzur, Nice, France There is a dearth of resources for data scientists, statisticians, etc., wishing to learn about Julia. Using well known data science methods, this book will both motivate the reader and assuage any unease. The book will get readers up to speed on key features of the Julia language and illustrate some of its advantages for data science work. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
EUR 69,16
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Añadir al carritoCondición: New. This is a great overview of the field of model-based clustering and classification by one of its leading developers. McNicholas provides a resource that I am certain will be used by researchers in statistics and related disciplines for quite some ti.
EUR 78,36
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Añadir al carritoCondición: New. Paul D. McNicholas is the Canada Research Chair in Computational Statistics at McMaster University, where he is a Professor in the Department of Mathematics and Statistics. Peter Tait is a Ph.D. student at the Department of.
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Añadir al carritoPaperback. Condición: Brand New. 217 pages. 8.25x5.50x0.50 inches. In Stock.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 130,30
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Añadir al carritoCondición: New.
Idioma: Inglés
Publicado por Taylor & Francis Group, 2016
ISBN 10: 1482225662 ISBN 13: 9781482225662
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 129,12
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Añadir al carritoCondición: New. pp. 210.
Idioma: Inglés
Publicado por Taylor & Francis Group, 2016
ISBN 10: 1482225662 ISBN 13: 9781482225662
Librería: Majestic Books, Hounslow, Reino Unido
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Añadir al carritoCondición: New. pp. 210.
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Añadir al carritoTaschenbuch. Condición: Neu. Mixture Model-Based Classification | Paul D. McNicholas | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | Taylor & Francis | EAN 9780367736958 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Añadir al carritoHardcover. Condición: Brand New. 212 pages. 9.25x6.25x0.75 inches. In Stock.
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