Julia Quick Syntax Reference
A Pocket Guide for Data Science Programming
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24 959 Ft (23 771 Ft + 5% VAT)
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- Discount 8% (cc. 1 997 Ft off)
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24 959 Ft
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
Not in stock at Prospero.
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Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number Second Edition
- Publisher Apress
- Date of Publication 4 January 2025
- Number of Volumes 1 pieces, Book
- ISBN 9798868809644
- Binding Paperback
- No. of pages361 pages
- Size 235x155 mm
- Language English
- Illustrations 83 Illustrations, black & white 676
Categories
Short description:
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.
MoreLong description:
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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Table of Contents:
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.
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