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Julia Quick Syntax Reference
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Julia Quick Syntax Reference : A Pocket Guide for Data Science Programming

Second Edition

Book Details

Format Paperback / Softback
ISBN-10 886880964Y
ISBN-13 9798868809644
Edition Second Edition
Publisher Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint APress
Country of Manufacture GB
Country of Publication GB
Publication Date Jan 28th, 2025
Print length 361 Pages
Ksh 9,000.00 Werezi Extended Catalogue

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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 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.  

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 Learn  

  • Work with Julia types and the different containers for rapid development
  • Use vectorized, classical loop-based code, logical operators, and blocks
  • Explore Julia functions: arguments, return values, polymorphism, parameters, anonymous functions, and broadcasts
  • Build custom structures in Julia
  • Use 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 Plots
  • Implement complete ML workflows with BetaML, from data coding to model evaluation, and more.

Who This Book Is For

Experienced 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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