Overview
- The first quick reference on Julia
- Provides important information as quickly as possible
- Contains information for today's data scientists and programmers
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About this book
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 (13 chapters)
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Front Matter
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Packages Ecosystem
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Front Matter
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Back Matter
Authors and Affiliations
About the author
Antonello Lobianco, PhD is a research engineer employed by a French Grande É cole (polytechnic university). He works on the biophysical and economic modelling of the forest sector and is responsible for the lab models portfolio. He does programming in C++, Perl, PHP, Visual Basic, Python, and Julia. He teaches environmental and forest economics at undergraduate and graduate levels and modelling at PhD level. For a few years, he has followed the development of Julia as it fits his modelling needs. He is the author of a few Julia packages, particularly on data analysis and machine learning (search sylvaticus on GitHub).
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Bibliographic Information
Book Title: Julia Quick Syntax Reference
Book Subtitle: A Pocket Guide for Data Science Programming
Authors: Antonello Lobianco
DOI: https://doi.org/10.1007/979-8-8688-0965-1
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Antonello Lobianco 2024
Softcover ISBN: 979-8-8688-0964-4Published: 04 January 2025
eBook ISBN: 979-8-8688-0965-1Published: 03 January 2025
Edition Number: 2
Number of Pages: XV, 361
Number of Illustrations: 83 b/w illustrations
Topics: Programming Languages, Compilers, Interpreters, Artificial Intelligence, Data Mining and Knowledge Discovery, Mathematics of Computing