Julia Quick Syntax Reference - Lobianco, Antonello; - Prospero Internetes Könyváruház

 
A termék adatai:

ISBN13:9798868809644
ISBN10:8868809648
Kötéstípus:Puhakötés
Terjedelem:315 oldal
Méret:235x155 mm
Nyelv:angol
Illusztrációk: 28 Illustrations, color
700
Témakör:

Julia Quick Syntax Reference

A Pocket Guide for Data Science Programming
 
Kiadás sorszáma: Second Edition
Kiadó: Apress
Megjelenés dátuma:
Kötetek száma: 1 pieces, Book
 
Normál ár:

Kiadói listaár:
EUR 58.84
Becsült forint ár:
25 083 Ft (23 889 Ft + 5% áfa)
Miért becsült?
 
Az Ön ára:

20 067 (19 111 Ft + 5% áfa )
Kedvezmény(ek): 20% (kb. 5 017 Ft)
A kedvezmény érvényes eddig: 2024. december 31.
A kedvezmény csak az 'Értesítés a kedvenc témákról' hírlevelünk címzettjeinek rendeléseire érvényes.
Kattintson ide a feliratkozáshoz
 
Beszerezhetőség:

Még nem jelent meg, de rendelhető. A megjelenéstől számított néhány héten belül megérkezik.
 
  példányt

 
Rövid leírás:

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.

Hosszú leírás:

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.



 

Tartalomjegyzék:

Part 1.  Language Core.- 1. Getting Started.- 2. Data Types and Structures.- 3. Control Flow and Functions.- 4. Custom Types.-  E1: Shelling Segregation Model - 5. Input ? Output.- 6. Metaprogramming and Macros.- 7. Interfacing Julia with Other Languages.- 8. Efficiently Write Efficient Code. - 9 Parallel Computing in Julia - Part 2. Packages Ecosystem.- 10. Working with Data.- 11. Scientific Libraries.- E2: Fitting a forest growth model - 12 ? AI with Julia ? E3. Predict house values - 13. Utilities. Appendix: Solutions to the exercises.