Julia quick syntax reference de lobianco antonello (19 resultados)
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Paperback. Condición: New. Second Edition. Learn the Julia programming language as quickly as possible. This book is a must-have reference guide that presents the essential Julia syntax in a well-organized format, updated with the latest features of Julia's APIs, libraries, and packages.This book provides an introduction that re…veals basic Julia structures and syntax; discusses data types, control flow, functions, input/output, exceptions, metaprogramming, performance, and more. Additionally, you'll learn to interface Julia with other programming languages such as R for statistics or Python. At a more applied level, you will learn how to use Julia packages for data analysis, numerical optimization, symbolic computation, and machine learning, and how to present your results in dynamic documents.The Second Edition delves deeper into modules, environments, and parallelism in Julia. It covers random numbers, reproducibility in stochastic computations, and adds a section on probabilistic analysis. Finally, it provides forward-thinking introductions to AI and machine learning workflows using BetaML, including regression, classification, clustering, and more, with practical exercises and solutions for self-learners.What You Will Learn Work with Julia types and the different containers for rapid developmentUse vectorized, classical loop-based code, logical operators, and blocksExplore Julia functions: arguments, return values, polymorphism, parameters, anonymous functions, and broadcastsBuild custom structures in JuliaUse C/C++, Python or R libraries in Julia and embed Julia in other code.Optimize performance with GPU programming, profiling and more.Manage, prepare, analyse and visualise your data with DataFrames and PlotsImplement complete ML workflows with BetaML, from data coding to model evaluation, and more.Who This Book Is ForExperienced programmers who are new to Julia, as well as data scientists who want to improve their analysis or try out machine learning algorithms with Julia.
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Paperback. Condición: New. Second Edition. Learn the Julia programming language as quickly as possible. This book is a must-have reference guide that presents the essential Julia syntax in a well-organized format, updated with the latest features of Julia's APIs, libraries, and packages.This book provides an introduction that re…veals basic Julia structures and syntax; discusses data types, control flow, functions, input/output, exceptions, metaprogramming, performance, and more. Additionally, you'll learn to interface Julia with other programming languages such as R for statistics or Python. At a more applied level, you will learn how to use Julia packages for data analysis, numerical optimization, symbolic computation, and machine learning, and how to present your results in dynamic documents.The Second Edition delves deeper into modules, environments, and parallelism in Julia. It covers random numbers, reproducibility in stochastic computations, and adds a section on probabilistic analysis. Finally, it provides forward-thinking introductions to AI and machine learning workflows using BetaML, including regression, classification, clustering, and more, with practical exercises and solutions for self-learners.What You Will Learn Work with Julia types and the different containers for rapid developmentUse vectorized, classical loop-based code, logical operators, and blocksExplore Julia functions: arguments, return values, polymorphism, parameters, anonymous functions, and broadcastsBuild custom structures in JuliaUse C/C++, Python or R libraries in Julia and embed Julia in other code.Optimize performance with GPU programming, profiling and more.Manage, prepare, analyse and visualise your data with DataFrames and PlotsImplement complete ML workflows with BetaML, from data coding to model evaluation, and more.Who This Book Is ForExperienced programmers who are new to Julia, as well as data scientists who want to improve their analysis or try out machine learning algorithms with Julia.
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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Librería: Shakespeare Book House, Rockford, IL, Estados Unidos de AmericaShakespeare Book House
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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Librería: Studibuch, Stuttgart, AlemaniaStudibuch
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paperback. Condición: Gut. 236 Seiten; 9781484251898.3 Gewicht in Gramm: 500.
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Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de AmericaRarewaves USA United
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Paperback. Condición: New. Second Edition. Learn the Julia programming language as quickly as possible. This book is a must-have reference guide that presents the essential Julia syntax in a well-organized format, updated with the latest features of Julia's APIs, libraries, and packages.This book provides an introduction that re…veals basic Julia structures and syntax; discusses data types, control flow, functions, input/output, exceptions, metaprogramming, performance, and more. Additionally, you'll learn to interface Julia with other programming languages such as R for statistics or Python. At a more applied level, you will learn how to use Julia packages for data analysis, numerical optimization, symbolic computation, and machine learning, and how to present your results in dynamic documents.The Second Edition delves deeper into modules, environments, and parallelism in Julia. It covers random numbers, reproducibility in stochastic computations, and adds a section on probabilistic analysis. Finally, it provides forward-thinking introductions to AI and machine learning workflows using BetaML, including regression, classification, clustering, and more, with practical exercises and solutions for self-learners.What You Will Learn Work with Julia types and the different containers for rapid developmentUse vectorized, classical loop-based code, logical operators, and blocksExplore Julia functions: arguments, return values, polymorphism, parameters, anonymous functions, and broadcastsBuild custom structures in JuliaUse C/C++, Python or R libraries in Julia and embed Julia in other code.Optimize performance with GPU programming, profiling and more.Manage, prepare, analyse and visualise your data with DataFrames and PlotsImplement complete ML workflows with BetaML, from data coding to model evaluation, and more.Who This Book Is ForExperienced programmers who are new to Julia, as well as data scientists who want to improve their analysis or try out machine learning algorithms with Julia.
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Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
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Paperback. Condición: New. Second Edition. Learn the Julia programming language as quickly as possible. This book is a must-have reference guide that presents the essential Julia syntax in a well-organized format, updated with the latest features of Julia's APIs, libraries, and packages.This book provides an introduction that re…veals basic Julia structures and syntax; discusses data types, control flow, functions, input/output, exceptions, metaprogramming, performance, and more. Additionally, you'll learn to interface Julia with other programming languages such as R for statistics or Python. At a more applied level, you will learn how to use Julia packages for data analysis, numerical optimization, symbolic computation, and machine learning, and how to present your results in dynamic documents.The Second Edition delves deeper into modules, environments, and parallelism in Julia. It covers random numbers, reproducibility in stochastic computations, and adds a section on probabilistic analysis. Finally, it provides forward-thinking introductions to AI and machine learning workflows using BetaML, including regression, classification, clustering, and more, with practical exercises and solutions for self-learners.What You Will Learn Work with Julia types and the different containers for rapid developmentUse vectorized, classical loop-based code, logical operators, and blocksExplore Julia functions: arguments, return values, polymorphism, parameters, anonymous functions, and broadcastsBuild custom structures in JuliaUse C/C++, Python or R libraries in Julia and embed Julia in other code.Optimize performance with GPU programming, profiling and more.Manage, prepare, analyse and visualise your data with DataFrames and PlotsImplement complete ML workflows with BetaML, from data coding to model evaluation, and more.Who This Book Is ForExperienced programmers who are new to Julia, as well as data scientists who want to improve their analysis or try out machine learning algorithms with Julia.
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Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Learn the Julia programming language as quickly as possible. This book is a must-have reference guide that presents the essential Julia syntax in a well-organized format, updated with the latest features of Julia's APIs, libraries, and…packages.This book provides an introduction that reveals basic Julia structures and syntax; discusses data types, control flow, functions, input/output, exceptions, metaprogramming, performance, and more. Additionally, you'll learn to interface Julia with other programming languages such as R for statistics or Python. At a more applied level, you will learn how to use Julia packages for data analysis, numerical optimization, symbolic computation, and machine learning, and how to present your results in dynamic documents.The Second Edition delves deeper into modules, environments, and parallelism in Julia. It covers random numbers, reproducibility in stochastic computations, and adds a section on probabilistic analysis. Finally, it provides forward-thinking introductions to AI and machine learning workflows using BetaML, including regression, classification, clustering, and more, with practical exercises and solutions for self-learners.What You Will LearnWork with Julia types and the different containers for rapid developmentUse vectorized, classical loop-based code, logical operators, and blocksExplore Julia functions: arguments, return values, polymorphism, parameters, anonymous functions, and broadcastsBuild custom structures in JuliaUse C/C++, Python or R libraries in Julia and embed Julia in other code.Optimize performance with GPU programming, profiling and more.Manage, prepare, analyse and visualise your data with DataFrames and PlotsImplement complete ML workflows with BetaML, from data coding to model evaluation, and more.Who This Book Is ForExperienced programmers who are new to Julia, as well as data scientists who want to improve their analysis or try out machine learning algorithms with Julia.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 384 pp. Englisch.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Learn the Julia programming language as quickly as possible. This book is a must-have reference guide that presents the essential Julia syntax in a well-organized format, updated with the latest features of Julia's APIs, libraries, and p…ackages.This book provides an introduction that reveals basic Julia structures and syntax; discusses data types, control flow, functions, input/output, exceptions, metaprogramming, performance, and more. Additionally, you'll learn to interface Julia with other programming languages such as R for statistics or Python. At a more applied level, you will learn how to use Julia packages for data analysis, numerical optimization, symbolic computation, and machine learning, and how to present your results in dynamic documents.The Second Edition delves deeper into modules, environments, and parallelism in Julia. It covers random numbers, reproducibility in stochastic computations, and adds a section on probabilistic analysis. Finally, it provides forward-thinking introductions to AI and machine learning workflows using BetaML, including regression, classification, clustering, and more, with practical exercises and solutions for self-learners.What You Will LearnWork with Julia types and the different containers for rapid developmentUse vectorized, classical loop-based code, logical operators, and blocksExplore Julia functions: arguments, return values, polymorphism, parameters, anonymous functions, and broadcastsBuild custom structures in JuliaUse C/C++, Python or R libraries in Julia and embed Julia in other code.Optimize performance with GPU programming, profiling and more.Manage, prepare, analyse and visualise your data with DataFrames and PlotsImplement complete ML workflows with BetaML, from data coding to model evaluation, and more.Who This Book Is ForExperienced programmers who are new to Julia, as well as data scientists who want to improve their analysis or try out machine learning algorithms with Julia.
