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Best Julia Books 2024

Julia is a high-level dynamic programming language designed for use in high-performance numerical analysis. Julia was first released in 2012 by Jeff Bezanson, Alan Edelman, Stefan Karpinski and Viral B. Shah. Some distinctive features include a sophisticated compiler, distributed parallel execution, numerical accuracy, extensive mathematical function library, etc.

Practical Julia: A Hands-On Introduction for Scientific Minds

Practical Julia: A Hands-On Introduction for Scientific Minds
  • Phillips, Lee (Author)
  • English (Publication Language)
  • 528 Pages - 10/31/2023 (Publication Date) - No Starch Press (Publisher)

by Lee Phillips. Dive in with a thorough guide to Julia’s syntax, data types, and best practices, then transition to craft solutions for challenges in physics, statistics, biology, mathematics, scientific machine learning, and more. Whether you’re solving computational problems, visualizing data, writing simulations, or developing specialized tools, Practical Julia will show you how. As you work through the book, you’ll:

• Use comprehensions and generators, higher-level functions, array initialization and manipulation, and perform operations on Unicode text
• Create new syntax and generate code with metaprogramming and macros, and control the error system to manipulate program execution
• Visualize everything from mathematical constructs and experimental designs to algorithm flowcharts
• Elevate performance using Julia’s unique type system with multiple dispatch
• Delve into scientific packages tailored for diverse fields like fluid dynamics, agent-based modeling, and image processing

Julia as a Second Language: General purpose programming with a taste of data science

Julia as a Second Language: General purpose programming with a taste of data science
  • Engheim, Erik (Author)
  • English (Publication Language)
  • 400 Pages - 05/02/2023 (Publication Date) - Manning (Publisher)

by Erik Engheim introduces Julia to readers with a beginning-level knowledge of another language like Python or JavaScript. You’ll learn by coding engaging hands-on projects that encourage you to apply what you’re learning immediately. Don’t be put off by Julia’s reputation as a scientific programming language—there’s no data science or numerical computing knowledge required. You can get started with what you learned in high school math classes. You will:

Data types like numbers, strings, arrays, and dictionaries
Immediate feedback with Julia’s read-evaluate-print-loop (REPL)
Simplify code interactions with multiple dispatch
Sharing code using modules and packages
Object-oriented and functional programming styles

Think Julia: How to Think Like a Computer Scientist

Think Julia: How to Think Like a Computer Scientist
  • Lauwens, Ben (Author)
  • English (Publication Language)
  • 295 Pages - 05/28/2019 (Publication Date) - O'Reilly Media (Publisher)

by Ben Lauwens and Allen B. Downey uses Julia 1.0 to guide you through programming step by step, starting with programming basics before moving on to more advanced features like creating new types and sending multiples. Designed from the ground up for high performance, Julia is a versatile language ideal not only for numerical analysis and computer science, but also for web programming and scripting. Through the exercises in each chapter, you will test programming concepts as you learn them. This is one of the best books to learn is perfect for high school or college students, as well as self-taught and professionals who need to learn the basics of programming.

Start with the basics, including the syntax and semantics of the scripting language.
Get a clear definition of each programming concept
Discover values, variables, declarations, functions, and data structures in a logical progression
Find out how to work with files and databases
Understand types, methods, and multiple shipments
Use debugging techniques to correct syntax, runtime, and semantic errors
Explore interface design and data structures through case studies

Julia Programming Projects: Learn Julia 1.x by building apps for data analysis, visualization, machine learning, and the web

Julia 1.0 By Example
  • Adrian Salceanu (Author)
  • English (Publication Language)
  • 378 Pages - 05/09/2018 (Publication Date) - Packt Publishing - ebooks Account (Publisher)

by Adrian Salceanu illustrates how to analyze the Iris dataset using DataFrames. You will explore the functions and type system, methods, and multiple shipping while building a web scraper and web application. Next, you will dive into machine learning, where you will create a book recommendation system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco enterprise database. After meta-programming, the final chapters will cover dates and times, time series analysis, visualization, and forecasting. You will:

Leverage Julia’s strengths, best packages, and best IDE options
Analyze and manipulate data sets with Julia and DataFrames
Write complex code while creating real Julia applications
Develop and run a web application using Julia and the HTTP package
Build a recommendation system using supervised machine learning
Perform exploratory data analysis
Apply unsupervised machine learning algorithms
Perform analysis, visualization and forecasting of time series data.

Julia High Performance: Optimizations, distributed computing, multithreading, and GPU programming with Julia 1.0 and beyond, 2nd Edition

Julia High Performance
  • Sengupta, Avik (Author)
  • English (Publication Language)
  • 218 Pages - 06/11/2019 (Publication Date) - Packt Publishing (Publisher)

by Avik Sengupta begins with an explanation of how Julia uses type information to achieve its performance goals, as well as how to leverage multiple dispatches to assist the compiler in producing high-performance machine code. Following that, you’ll learn how to analyze Julia programs and spot problems with time and memory usage. We show you how to accurately use Julia’s typing features to develop high-performance code, as well as how the Julia compiler leverages type information to generate fast machine code.

Moving on, you’ll master design constraints and discover how to exploit the GPU’s power in Julia code, as well as compile Julia code to the GPU directly. Then you’ll learn how to leverage Julia’s shared memory multithreading and how to employ tasks and asynchronous IO to develop responsive programs. You’ll get a taste of Julia’s distributed computing capabilities at the conclusion, as well as how to run Julia programs on a big distributed cluster.

Hands-On Design Patterns and Best Practices with Julia: Proven solutions to common problems in software design for Julia 1.x

Hands-On Design Patterns and Best Practices with Julia
  • Kwong, Tom (Author)
  • English (Publication Language)
  • 532 Pages - 01/17/2020 (Publication Date) - Packt Publishing (Publisher)

by Tom Kwong will demonstrate how to take advantage of design patterns with real-world applications. Starting with an overview of design patterns and application design best practices, you will learn about some of Julia’s more fundamental features, such as modules, data types, functions / interfaces, and meta-programming. You will then become familiar with modern Julia design patterns to create large-scale applications with an emphasis on performance, reusability, robustness, and maintainability. The best book to learn julia also covers anti-patterns and how to avoid common errors and pitfalls in development. You will see how traditional object-oriented models can be implemented differently and more efficiently in Julia. Finally, you will explore various use cases and examples, such as how expert Julia developers use modern Julia design patterns in their open source Julia programs. You will:

Master the Julia language features that are essential for large-scale software application development.
Discover design patterns to improve the overall architecture and design of your application
Develop reusable programs that are modular, scalable, efficient, and easy to maintain.
Weighing the pros and cons of using different design patterns for use cases
Explore methods for moving from object-oriented programming to using equivalent or more advanced Julia techniques

The Little Book of Julia Algorithms: A workbook to develop fluency in Julia programming

The Little Book of Julia Algorithms: A workbook to develop fluency in Julia programming
  • Sengupta, Ahan (Author)
  • English (Publication Language)
  • 120 Pages - 09/13/2020 (Publication Date) - SAV Publishing (Publisher)

by Ahan Sengupta, William Lau attempts to teach the Julia programming language while explaining essential computer science topics. Julia is a good high-level language for beginners because it is quick and productive. Programming has a high learning curve, and this book seeks to make it easier by encouraging practice and progressively introducing more complicated topics. The book includes 50 programming tasks to inspire readers to create their own programs. All of the obstacles have solutions at the end of the book. This book will prepare readers for more advanced programming in college math, science, or computer science classes by making them comfortable with utilizing computers to solve any problem. The reader should be more comfortable with: •Loops and conditionals after completing the tasks in this book. •Simple Statistics and Plotting •Structuring code using functions •Reading and writing files •Installing and using packages •Sorting and searching

Julia 1.0 Programming: Dynamic and high-performance programming to build fast scientific applications, 2nd Edition

Julia 1.0 Programming
  • Balbaert, Ivo (Author)
  • English (Publication Language)
  • 196 Pages - 09/28/2018 (Publication Date) - Packt Publishing (Publisher)

by Ivo Balbaert starts by learning how to set up a running Julia platform, before exploring its different built-in types. With practical examples, this book guides you through two important types of collections: tables and matrices. In addition, you will learn how type conversions and promotions work.

During the course of the julia language books, you will be introduced to the concepts of homo-iconicity and metaprogramming in Julia. You will understand how Julia offers different ways of interacting with an operating system, as well as other languages, and then you will discover what macros are. Once you understand the basics, you will study what makes Julia suitable for numerical computing, technical computing, distributed computing and scientific computing, and learn about the features that Julia offers. By the end of this book, you will also have learned how to run external programs. You will learn:

Configure your Julia environment for high productivity
Create your own types to extend the built-in type system
Visualize your data in Julia with tracking packages
Explore the use of built-in macros for testing and debugging, among other uses
Apply Julia to solve problems simultaneously
Integrate Julia with other languages ​​like C, Python, and MATLAB

Tanmay Teaches Julia for Beginners: A Springboard to Machine Learning for All Ages

Tanmay Teaches Julia for Beginners: A Springboard to Machine Learning for All Ages
  • Bakshi, Tanmay (Author)
  • English (Publication Language)
  • 192 Pages - 11/29/2019 (Publication Date) - McGraw Hill TAB (Publisher)

by Tanmay Bakshi shows step-by-step how to create custom programs using Julia, the intuitive open source programming language. Written by 15-year-old tech phenom Tanmay Bakshi, the best julia programming language books is presented in an accessible style that makes learning easy and enjoyable. The great book clearly explains Julia’s programming basics and reviews cutting edge machine learning apps. You will learn how to interface his Julia applications with code written in Python.

You will learn to:

• Install and configure your Julia environment
• Get started writing your own Julia applications
• Define variables and use them in your programs
• Use conditions, iterations, for loops, and while loops
• Create, browse, and modify tables
• Create an application to manage the things you lend and charge to your friends
• Create and use dictionaries
• Simplify the maintenance of your code thanks to the functions
• Apply functions in arrays and use functions recursively and generically

The Julia Language Handbook

The Julia Language Handbook
  • Root, George (Author)
  • English (Publication Language)
  • 199 Pages - 02/02/2019 (Publication Date) - Independently published (Publisher)

by George Root is updated from Julia v1.02 and all the examples, of which there are dozens, have been tested and all work. You will learn how to install and use the Julia REPL mode and the Jupyter Notebook mode to create and test your code. Other topics include: Data types Functions and packages Tuples Data matrices Data frames Data structures Flow control Loops and iteration Input / Output: formatted printing: writing and reading data files Line and scatter graphs Other types of graphs Random Numbers Optimizing with Optim and JuMP. This is one of the best julia programming books.

Julia Programming for Operations Research

Julia Programming for Operations Research
  • Kwon, Changhyun (Author)
  • English (Publication Language)
  • 262 Pages - 03/03/2019 (Publication Date) - Independently published (Publisher)

The main motivation of writing this book was to help the author himself. He is a professor in the field of operations research, and his daily activities involve building models of mathematical optimization, developing algorithms for solving the problems, implementing those algorithms using computer programming languages, experimenting with data, etc. Three languages are involved: human language, mathematical language, and computer language. His team of students need to go over three different languages, which requires “translation” among the three languages. As this book was written to teach his research group how to translate, this book will also be useful for anyone who needs to learn how to translate in a similar situation.

The Julia Language is as fast as C, as convenient as MATLAB, and as general as Python with a flexible algebraic modeling language for mathematical optimization problems. With the great support from Julia developers, especially the developers of the JuMP—Julia for Mathematical Programming—package, Julia makes a perfect tool for students and professionals in operations research and related areas such as industrial engineering, management science, transportation engineering, economics, and regional science.

Frequently Asked Questions

What is Julia programming language?

Julia is a high-performance dynamic programming language that is open source. It’s a general-purpose programming language that may be used to create any type of application. Many of its qualities, on the other hand, are useful for numerical analysis and computational research.

Who created Julia?

Julia was created by Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman. It was first released in 2012.

What are the main features of Julia?

Julia is known for its high-performance, just-in-time (JIT) compilation, multiple dispatch, and a rich ecosystem of packages for scientific and technical computing. It also has a clean and readable syntax, making it accessible to a wide range of users.

Where is Julia commonly used in?

Julia is often used in scientific computing, data science, machine learning, and numerical analysis. It’s popular in fields like physics, engineering, finance, and bioinformatics.

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