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| julia programming language tutorial: Hands-On Design Patterns and Best Practices with Julia Tom Kwong, 2020-01-17 Design and develop high-performance, reusable, and maintainable applications using traditional and modern Julia patterns with this comprehensive guide Key FeaturesExplore useful design patterns along with object-oriented programming in Julia 1.0Implement macros and metaprogramming techniques to make your code faster, concise, and efficientDevelop the skills necessary to implement design patterns for creating robust and maintainable applicationsBook Description Design patterns are fundamental techniques for developing reusable and maintainable code. They provide a set of proven solutions that allow developers to solve problems in software development quickly. This book will demonstrate how to leverage design patterns with real-world applications. Starting with an overview of design patterns and best practices in application design, you'll learn about some of the most fundamental Julia features such as modules, data types, functions/interfaces, and metaprogramming. You'll then get to grips with the modern Julia design patterns for building large-scale applications with a focus on performance, reusability, robustness, and maintainability. The book also covers anti-patterns and how to avoid common mistakes and pitfalls in development. You'll see how traditional object-oriented patterns can be implemented differently and more effectively in Julia. Finally, you'll explore various use cases and examples, such as how expert Julia developers use design patterns in their open source packages. By the end of this Julia programming book, you'll have learned methods to improve software design, extensibility, and reusability, and be able to use design patterns efficiently to overcome common challenges in software development. What you will learnMaster the Julia language features that are key to developing large-scale software applicationsDiscover design patterns to improve overall application architecture and designDevelop reusable programs that are modular, extendable, performant, and easy to maintainWeigh up the pros and cons of using different design patterns for use casesExplore methods for transitioning from object-oriented programming to using equivalent or more advanced Julia techniquesWho this book is for This book is for beginner to intermediate-level Julia programmers who want to enhance their skills in designing and developing large-scale applications. |
| julia programming language tutorial: Statistics with Julia Yoni Nazarathy, Hayden Klok, 2021-09-04 This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics. The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book’s associated GitHub repository online. See what co-creators of the Julia language are saying about the book: Professor Alan Edelman, MIT: With “Statistics with Julia”, Yoni and Hayden have written an easy to read, well organized, modern introduction to statistics. The code may be looked at, and understood on the static pages of a book, or even better, when running live on a computer. Everything you need is here in one nicely written self-contained reference. Dr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language. This book has been perfected through iteration over several semesters in the classroom. It prepares the reader with two complementary skills - statistical reasoning with hands on experience and working with large datasets through training in Julia. |
| julia programming language tutorial: Julia Programming for Operations Research Changhyun Kwon, 2019-03-03 Last Updated: December 2020 Based on Julia v1.3+ and JuMP v0.21+ 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. For more information, visit: http://www.chkwon.net/julia |
| julia programming language tutorial: Tanmay Teaches Julia for Beginners: A Springboard to Machine Learning for All Ages Tanmay Bakshi, 2019-12-06 Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. A quick guide to start writing your own fun and useful Julia apps—no prior experience required! This engaging guide shows, step by step, how to build custom programs using Julia, the open-source, intuitive scripting language. Written by 15-year-old technology phenom Tanmay Bakshi, the book is presented in an accessible style that makes learning easy and enjoyable. Tanmay Teaches Julia for Beginners: A Springboard to Machine Learning for All Ages clearly explains the basics of Julia programming and takes a look at cutting-edge machine learning applications. You will also discover how to interface your Julia apps with code written in Python. Inside, you’ll learn to: • Set up and configure your Julia environment • Get up and running writing your own Julia apps • Define variables and use them in your programs • Use conditions, iterations, for-loops, and while-loops • Create, go through, and modify arrays • Build an app to manage things you lend and get back from your friends • Create and utilize dictionaries • Simplify maintenance of your code using functions • Apply functions on arrays and use functions recursively and generically • Understand and program basic machine learning apps |
| julia programming language tutorial: Modern Fortran Milan Curcic, 2020-10-07 Modern Fortran teaches you to develop fast, efficient parallel applications using twenty-first-century Fortran. In this guide, you’ll dive into Fortran by creating fun apps, including a tsunami simulator and a stock price analyzer. Filled with real-world use cases, insightful illustrations, and hands-on exercises, Modern Fortran helps you see this classic language in a whole new light. Summary Using Fortran, early and accurate forecasts for hurricanes and other major storms have saved thousands of lives. Better designs for ships, planes, and automobiles have made travel safer, more efficient, and less expensive than ever before. Using Fortran, low-level machine learning and deep learning libraries provide incredibly easy, fast, and insightful analysis of massive data. Fortran is an amazingly powerful and flexible programming language that forms the foundation of high performance computing for research, science, and industry. And it's come a long, long way since starting life on IBM mainframes in 1956. Modern Fortran is natively parallel, so it's uniquely suited for efficiently handling problems like complex simulations, long-range predictions, and ultra-precise designs. If you're working on tasks where speed, accuracy, and efficiency matter, it's time to discover—or re-discover—Fortran.. About the technology For over 60 years Fortran has been powering mission-critical scientific applications, and it isn't slowing down yet! Rock-solid reliability and new support for parallel programming make Fortran an essential language for next-generation high-performance computing. Simply put, the future is in parallel, and Fortran is already there. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Modern Fortran teaches you to develop fast, efficient parallel applications using twenty-first-century Fortran. In this guide, you'll dive into Fortran by creating fun apps, including a tsunami simulator and a stock price analyzer. Filled with real-world use cases, insightful illustrations, and hands-on exercises, Modern Fortran helps you see this classic language in a whole new light. What's inside Fortran's place in the modern world Working with variables, arrays, and functions Module development Parallelism with coarrays, teams, and events Interoperating Fortran with C About the reader For developers and computational scientists. No experience with Fortran required. About the author Milan Curcic is a meteorologist, oceanographer, and author of several general-purpose Fortran libraries and applications. Table of Contents PART 1 - GETTING STARTED WITH MODERN FORTRAN 1 Introducing Fortran 2 Getting started: Minimal working app PART 2 - CORE ELEMENTS OF FORTRAN 3 Writing reusable code with functions and subroutines 4 Organizing your Fortran code using modules 5 Analyzing time series data with arrays 6 Reading, writing, and formatting your data PART 3 - ADVANCED FORTRAN USE 7 Going parallel with Fortan coarrays 8 Working with abstract data using derived types 9 Generic procedures and operators for any data type 10 User-defined operators for derived types PART 4 - THE FINAL STRETCH 11 Interoperability with C: Exposing your app to the web 12 Advanced parallelism with teams, events, and collectives |
| julia programming language tutorial: Machine Learning with R, the tidyverse, and mlr Hefin I. Rhys, 2020-03-31 Summary Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts. Once the domain of academic data scientists, machine learning has become a mainstream business process, and tools like the easy-to-learn R programming language put high-quality data analysis in the hands of any programmer. Machine Learning with R, the tidyverse, and mlr teaches you widely used ML techniques and how to apply them to your own datasets using the R programming language and its powerful ecosystem of tools. This book will get you started! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Machine Learning with R, the tidyverse, and mlr gets you started in machine learning using R Studio and the awesome mlr machine learning package. This practical guide simplifies theory and avoids needlessly complicated statistics or math. All core ML techniques are clearly explained through graphics and easy-to-grasp examples. In each engaging chapter, you’ll put a new algorithm into action to solve a quirky predictive analysis problem, including Titanic survival odds, spam email filtering, and poisoned wine investigation. What's inside Using the tidyverse packages to process and plot your data Techniques for supervised and unsupervised learning Classification, regression, dimension reduction, and clustering algorithms Statistics primer to fill gaps in your knowledge About the reader For newcomers to machine learning with basic skills in R. About the author Hefin I. Rhys is a senior laboratory research scientist at the Francis Crick Institute. He runs his own YouTube channel of screencast tutorials for R and RStudio. Table of contents: PART 1 - INTRODUCTION 1.Introduction to machine learning 2. Tidying, manipulating, and plotting data with the tidyverse PART 2 - CLASSIFICATION 3. Classifying based on similarities with k-nearest neighbors 4. Classifying based on odds with logistic regression 5. Classifying by maximizing separation with discriminant analysis 6. Classifying with naive Bayes and support vector machines 7. Classifying with decision trees 8. Improving decision trees with random forests and boosting PART 3 - REGRESSION 9. Linear regression 10. Nonlinear regression with generalized additive models 11. Preventing overfitting with ridge regression, LASSO, and elastic net 12. Regression with kNN, random forest, and XGBoost PART 4 - DIMENSION REDUCTION 13. Maximizing variance with principal component analysis 14. Maximizing similarity with t-SNE and UMAP 15. Self-organizing maps and locally linear embedding PART 5 - CLUSTERING 16. Clustering by finding centers with k-means 17. Hierarchical clustering 18. Clustering based on density: DBSCAN and OPTICS 19. Clustering based on distributions with mixture modeling 20. Final notes and further reading |
| julia programming language tutorial: Julia Quick Syntax Reference Antonello Lobianco, 2019-11-11 This quick Julia programming language guide is a condensed code and syntax reference to the Julia 1.x programming language, updated with the latest features of the Julia APIs, libraries, and packages. It presents the essential Julia syntax in a well-organized format that can be used as a handy reference. 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. You will learn how to use Julia packages for data analysis, numerical optimization and symbolic computation, and how to disseminate your results in dynamic documents or interactive web pages. In this book, the focus is on providing important information as quickly as possible. It is packed with useful information and is a must-have for any Julia programmer. What You Will Learn Set up the software needed to run Julia and your first Hello World example Work with types and the different containers that Julia makes available for rapid application development Use vectorized, classical loop-based code, logical operators, and blocks Explore Julia functions by looking at arguments, return values, polymorphism, parameters, anonymous functions, and broadcasts Build custom structures in Julia Interface Julia with other languages such as C/C++, Python, and R Program a richer API, modifying the code before it is executed using expressions, symbols, macros, quote blocks, and more Maximize your code’s performance Who This Book Is For Experienced programmers new to Julia, as well as existing Julia coders new tothe now stable Julia version 1.0 release. |
| julia programming language tutorial: Introduction to Applied Linear Algebra Stephen Boyd, Lieven Vandenberghe, 2018-06-07 A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples. |
| julia programming language tutorial: Numerical Methods for Scientific Computing Kyle Novak, 2022-03-13 A comprehensive guide to the theory, intuition, and application of numerical methods in linear algebra, analysis, and differential equations. With extensive commentary and code for three essential scientific computing languages: Julia, Python, and Matlab. |
| julia programming language tutorial: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| julia programming language tutorial: Learning Julia Anshul Joshi, Rahul Lakhanpal, 2017-11-24 Learn Julia language for data science and data analytics About This Book Set up Julia's environment and start building simple programs Explore the technical aspects of Julia and its potential when it comes to speed and data processing Write efficient and high-quality code in Julia Who This Book Is For This book allows existing programmers, statisticians and data scientists to learn the Julia and take its advantage while building applications with complex numerical and scientific computations. Basic knowledge of mathematics is needed to understand the various methods that will be used or created in the book to exploit the capabilities for which Julia is made. What You Will Learn Understand Julia's ecosystem and create simple programs Master the type system and create your own types in Julia Understand Julia's type system, annotations, and conversions Define functions and understand meta-programming and multiple dispatch Create graphics and data visualizations using Julia Build programs capable of networking and parallel computation Develop real-world applications and use connections for RDBMS and NoSQL Learn to interact with other programming languages–C and Python—using Julia In Detail Julia is a highly appropriate language for scientific computing, but it comes with all the required capabilities of a general-purpose language. It allows us to achieve C/Fortran-like performance while maintaining the concise syntax of a scripting language such as Python. It is perfect for building high-performance and concurrent applications. From the basics of its syntax to learning built-in object types, this book covers it all. This book shows you how to write effective functions, reduce code redundancies, and improve code reuse. It will be helpful for new programmers who are starting out with Julia to explore its wide and ever-growing package ecosystem and also for experienced developers/statisticians/data scientists who want to add Julia to their skill-set. The book presents the fundamentals of programming in Julia and in-depth informative examples, using a step-by-step approach. You will be taken through concepts and examples such as doing simple mathematical operations, creating loops, metaprogramming, functions, collections, multiple dispatch, and so on. By the end of the book, you will be able to apply your skills in Julia to create and explore applications of any domain. Style and approach This book demonstrates the basics of Julia along with some data structures and testing tools that will give you enough material to get started with the language from an application standpoint. |
| julia programming language tutorial: Individual-Based Models of Cultural Evolution Alberto Acerbi, Alex Mesoudi, Marco Smolla, 2022-07-05 Individual-Based Models of Cultural Evolution shows readers how to create individual-based models of cultural evolution using the programming language R. The field of cultural evolution has emerged in the last few decades as a thriving, interdisciplinary effort to understand cultural change and cultural diversity within an evolutionary framework and using evolutionary tools, concepts, and methods. Given its roots in evolutionary biology, much of cultural evolution is grounded in, or inspired by, formal models. Yet many researchers interested in cultural evolution come from backgrounds that lack training in formal modelling, such as psychology, anthropology or archaeology. This book addresses that gap. It provides example code in R for readers to run their own models, moving from very simple models of the basic processes of cultural evolution, such as biased transmission and cultural mutation, to more advanced topics such as the evolution of social learning, demographic effects, and social network analysis. Features of this book: Recreates existing models in the literature to show how these were created and to enable readers to have a better understanding of their significance and how to apply them to their own research questions Provides full R code to realize models and analyse and plot outputs, with line-by-line analysis Requires no previous knowledge of the field of cultural evolution, and only very basic programming knowledge This is an essential resource for researchers and students interested in cultural evolution, including disciplines such as psychology, anthropology, archaeology, and biology as well as sociology and digital humanities. |
| julia programming language tutorial: Programming Language Explorations Ray Toal, Sage Strieker, Marco Berardini, 2024-08-06 Programming Language Explorations helps its readers gain proficiency in programming language practice and theory by presenting both example-focused, chapter-length explorations of fourteen important programming languages and detailed discussions of the major concepts transcending multiple languages. A language-by-language approach is sandwiched between an introductory chapter that motivates and lays out the major concepts of the field and a final chapter that brings together all that was learned in the middle chapters into a coherent and organized view of the field. Each of the featured languages in the middle chapters is introduced with a common trio of example programs and followed by a tour of its basic language features and coverage of interesting aspects from its type system, functional forms, scoping rules, concurrency patterns, and metaprogramming facilities. These chapters are followed by a brief tour of over 40 additional languages designed to enhance the reader’s appreciation of the breadth of the programming language landscape and to motivate further study. Targeted to both professionals and advanced college undergraduates looking to expand the range of languages and programming patterns they can apply in their work and studies, the book pays attention to modern programming practices, keeps a focus on cutting-edge programming patterns, and provides many runnable examples, all of which are available in the book’s companion GitHub repository. The combination of conceptual overviews with exploratory example-focused coverage of individual programming languages provides its readers with the foundation for more effectively authoring programs, prompting AI programming assistants, and, perhaps most importantly, learning—and creating—new languages. |
| julia programming language tutorial: Handbook of Regression Modeling in People Analytics Keith McNulty, 2021-07-30 Despite the recent rapid growth in machine learning and predictive analytics, many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best ‘swiss army knife’ we have for answering these kinds of questions. This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python, ranging from simple hypothesis testing to advanced multivariate modelling. Although it is primarily focused on examples related to the analysis of people and talent, the methods easily transfer to any discipline. The book hits a ‘sweet spot’ where there is just enough mathematical theory to support a strong understanding of the methods, but with a step-by-step guide and easily reproducible examples and code, so that the methods can be put into practice immediately. This makes the book accessible to a wide readership, from public and private sector analysts and practitioners to students and researchers. Key Features: • 16 accompanying datasets across a wide range of contexts (e.g. academic, corporate, sports, marketing) • Clear step-by-step instructions on executing the analyses. • Clear guidance on how to interpret results. • Primary instruction in R but added sections for Python coders. • Discussion exercises and data exercises for each of the main chapters. • Final chapter of practice material and datasets ideal for class homework or project work. |
| julia programming language tutorial: Advanced R Hadley Wickham, 2015-09-15 An Essential Reference for Intermediate and Advanced R Programmers Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R. The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn: The fundamentals of R, including standard data types and functions Functional programming as a useful framework for solving wide classes of problems The positives and negatives of metaprogramming How to write fast, memory-efficient code This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does. |
| julia programming language tutorial: Programming Entity Framework Julia Lerman, 2009-01-29 If you use Entity Framework in Visual Studio 2008 and .NET 3.5, this is the book you want. Programming Entity Framework, 1st Edition offers experienced developers a thorough introduction to Microsoft's core framework for modeling and interacting with data in .NET applications. This hands-on tour provides a deep understanding of Entity Framework's architecture and APIs, and explains how to use the framework in a variety of applications built with Visual Studio 2008 and .NET 3.5. From the Entity Data Model (EDM) and Object Services to EntityClient and the Metadata Workspace, this highly acclaimed first edition covers it all. Understand the core concepts you need to make the best use of the Entity Framework (EF) in your applications Learn to query your data, using either LINQ to Entities or Entity SQL Create Windows Forms, WPF, and ASP.NET applications Build ASMX web services and WCF services Use Object Services to work directly with your entity objects Delve into model customization, relationship management, change tracking, data concurrency, and more One important note: while many of the lessons from this book will continue to be valuable as you move to .NET 4, the thoroughly revised second edition of Programming Entity Framework (August 2010) specifically targets Visual Studio 2010 and .NET 4 -- where there have been many advancements and additions to the framework. |
| julia programming language tutorial: Julia for Data Science Zacharias Voulgaris, 2016 After covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function. Specialized script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover: An overview of the data science pipeline along with an example illustrating the key points, implemented in Julia Options for Julia IDEs Programming structures and functions Engineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data preprocessing Data visualization and some simple yet powerful statistics for data exploration purposes Dimensionality reduction and feature evaluation Machine learning methods, ranging from unsupervised (different types of clustering) to supervised ones (decision trees, random forests, basic neural networks, regression trees, and Extreme Learning Machines) Graph analysis including pinpointing the connections among the various entities and how they can be mined for useful insights. Each chapter concludes with a series of questions and exercises to reinforce what you learned. The last chapter of the book will guide you in creating a data science application from scratch using Julia. |
| julia programming language tutorial: Programming with Java Julia Case Bradley, Anita C. Millspaugh, 2002 Java has become one of the leading development languages today. It plays a very important role in application development for business as well as a tool for Web programming. This Java text is designed primarily for business programming students. It assumes no prior programming experience and introduces students to the object-oriented approach from the very beginning. This text can be used for a first language course or for a more advanced programming course. |
| julia programming language tutorial: First Semester in Numerical Analysis with Julia Giray Ökten, 2019 |
| julia programming language tutorial: Julia High Performance Avik Sengupta, 2016-04-26 Design and develop high performing programs with Julia About This Book Learn to code high reliability and high performance programs Stand out from the crowd by developing code that runs faster than your peers' codes This book is intended for developers who are interested in high performance technical programming. Who This Book Is For This book is for beginner and intermediate Julia programmers who are interested in high performance technical computing. You will have a basic familiarity with Julia syntax, and have written some small programs in the language. What You Will Learn Discover the secrets behind Julia's speed Get a sense of the possibilities and limitations of Julia's performance Analyze the performance of Julia programs Measure the time and memory taken by Julia programs Create fast machine code using Julia's type information Define and call functions without compromising Julia's performance Understand number types in Julia Use Julia arrays to write high performance code Get an overview of Julia's distributed computing capabilities In Detail Julia is a high performance, high-level dynamic language designed to address the requirements of high-level numerical and scientific computing. Julia brings solutions to the complexities faced by developers while developing elegant and high performing code. Julia High Performance will take you on a journey to understand the performance characteristics of your Julia programs, and enables you to utilize the promise of near C levels of performance in Julia. You will learn to analyze and measure the performance of Julia code, understand how to avoid bottlenecks, and design your program for the highest possible performance. In this book, you will also see how Julia uses type information to achieve its performance goals, and how to use multuple dispatch to help the compiler to emit high performance machine code. Numbers and their arrays are obviously the key structures in scientific computing – you will see how Julia's design makes them fast. The last chapter will give you a taste of Julia's distributed computing capabilities. Style and approach This is a hands-on manual that will give you good explanations about the important concepts related to Julia programming. |
| julia programming language tutorial: Julia for Machine Learning Zacharias Voulgaris, 2020-05-18 Unleash the power of Julia for your machine learning tasks. We reveal why Julia is chosen for more and more data science and machine learning projects, including Julia's ability to run algorithms at lightning speed. Next, we show you how to set up Julia and various IDEs such as Jupyter. Afterward, we explore key Julia libraries, which are useful for data science work, including packages related to visuals, data structures, and mathematical processes. After building a foundation in Julia, we dive into machine learning, with foundational concepts reinforced by Julia use cases. The use cases build upon each other, reaching the level where we code a machine learning model from scratch using Julia. All of these use cases are available in a series of Jupyter notebooks. After covering dimensionality reduction methods, we explore additional machine learning topics, such as parallelization and data engineering. Although knowing how to use Julia is essential, it is even more important to communicate our results to the business, which we cover next, including how to work efficiently with project stakeholders. Our Julia journey then ascends to the finer points, including improving machine learning transparency, reconciling machine learning with statistics, and continuing to innovate with Julia. The final chapters cover future trends in the areas of Julia, machine learning, and artificial intelligence. We explain machine learning and Bayesian Statistics hybrid systems, and Julia's Gen language. We share many resources so you can continue to sharpen your Julia and machine learning skills. Each chapter concludes with a series of questions designed to reinforce that chapter's material, with answers provided in an appendix. Other appendices include an extensive glossary, bridge packages between Julia and other programming languages, and an overview of three data science-related heuristics implemented in Julia, which aren't in any of the existing packages. |
| julia programming language tutorial: The Grammar of Graphics Leland Wilkinson, 2006-01-28 Preface to First Edition Before writing the graphics for SYSTAT in the 1980’s, I began by teaching a seminar in statistical graphics and collecting as many different quantitative graphics as I could find. I was determined to produce a package that could draw every statistical graphic I had ever seen. The structure of the program was a collection of procedures named after the basic graph types they p- duced. The graphics code was roughly one and a half megabytes in size. In the early 1990’s, I redesigned the SYSTAT graphics package using - ject-based technology. I intended to produce a more comprehensive and - namic package. I accomplished this by embedding graphical elements in a tree structure. Rendering graphics was done by walking the tree and editing worked by adding and deleting nodes. The code size fell to under a megabyte. In the late 1990’s, I collaborated with Dan Rope at the Bureau of Labor Statistics and Dan Carr at George Mason University to produce a graphics p- duction library called GPL, this time in Java. Our goal was to develop graphics components. This book was nourished by that project. So far, the GPL code size is under half a megabyte. |
| julia programming language tutorial: Super Scratch Programming Adventure! (Scratch 3) The LEAD Project, 2019-08-27 Comics! Games! Programming! Now updated to cover Scratch 3. Scratch is the wildly popular educational programming language used by millions of first-time learners in classrooms and homes worldwide. By dragging together colorful blocks of code, kids can learn computer programming concepts and make cool games and animations. The latest version, Scratch 3, features an updated interface, new sprites and programming blocks, and extensions that let you program things like the micro:bit. In Super Scratch Programming Adventure!, kids learn programming fundamentals as they make their very own playable video games. They'll create projects inspired by classic arcade games that can be programmed (and played!) in an afternoon. Patient, step-by-step explanations of the code and fun programming challenges will have kids creating their own games in no time. This full-color comic book makes programming concepts like variables, flow control, and subroutines effortless to absorb. Packed with ideas for games that kids will be proud to show off, Super Scratch Programming Adventure! is the perfect first step for the budding programmer. Covers Scratch 3 |
| julia programming language tutorial: JDK 1.4 Tutorial Greg M. Travis, 2002 This book provides complete reference programs that use the powerful new features of JDK 1.4. These explain and illustrate the use of the new APIs, and can serve as the starting point for your own programs. |
| julia programming language tutorial: Algorithms for Optimization Mykel J. Kochenderfer, Tim A. Wheeler, 2019-03-12 A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals. |
| julia programming language tutorial: Programming GPUs Andrew Sheppard, 2012-12-15 GPUs may have started life as graphics processors, but recently they've emerged as a fantastic numerical co-processor for high-performance general applications on the CPU. This book not only teaches you the fundamentals of parallel programming with GPUs, it helps you think in parallel. You learn best practices, algorithms, and designs for achieving greater application performance with these processors. Amazon recently added GPU supercomputing to its cloud-computing platform—a clear sign that parallel programming is becoming an essential skill. This book includes valuable input from major CPU and GPU manufacturers—Intel, NVIDIA and AMD—to help experienced programmers get a head start on programming GPU applications. Understand the differences between parallel and sequential programming Learn about GPU architecture, including the runtime environment, threads, and memory Build and deploy GPU applications and libraries—and port existing applications Use debugging and profiling tools and techniques Write GPU programs for clusters and the cloud Design programs that will take advantage of future enhancements to GPU technology—including the trend of putting CPU and GPU cores on a single chip |
| julia programming language tutorial: Nonlinear Dynamics George Datseris, Ulrich Parlitz, 2022-03-14 This concise and up-to-date textbook provides an accessible introduction to the core concepts of nonlinear dynamics as well as its existing and potential applications. The book is aimed at students and researchers in all the diverse fields in which nonlinear phenomena are important. Since most tasks in nonlinear dynamics cannot be treated analytically, skills in using numerical simulations are crucial for analyzing these phenomena. The text therefore addresses in detail appropriate computational methods as well as identifying the pitfalls of numerical simulations. It includes numerous executable code snippets referring to open source Julia software packages. Each chapter includes a selection of exercises with which students can test and deepen their skills. |
| julia programming language tutorial: The standard arithmetic Ebenezer L. Jones, 1896 |
| julia programming language tutorial: Implementing Domain-Driven Design Vaughn Vernon, 2013-02-06 “For software developers of all experience levels looking to improve their results, and design and implement domain-driven enterprise applications consistently with the best current state of professional practice, Implementing Domain-Driven Design will impart a treasure trove of knowledge hard won within the DDD and enterprise application architecture communities over the last couple decades.” –Randy Stafford, Architect At-Large, Oracle Coherence Product Development “This book is a must-read for anybody looking to put DDD into practice.” –Udi Dahan, Founder of NServiceBus Implementing Domain-Driven Design presents a top-down approach to understanding domain-driven design (DDD) in a way that fluently connects strategic patterns to fundamental tactical programming tools. Vaughn Vernon couples guided approaches to implementation with modern architectures, highlighting the importance and value of focusing on the business domain while balancing technical considerations. Building on Eric Evans’ seminal book, Domain-Driven Design, the author presents practical DDD techniques through examples from familiar domains. Each principle is backed up by realistic Java examples–all applicable to C# developers–and all content is tied together by a single case study: the delivery of a large-scale Scrum-based SaaS system for a multitenant environment. The author takes you far beyond “DDD-lite” approaches that embrace DDD solely as a technical toolset, and shows you how to fully leverage DDD’s “strategic design patterns” using Bounded Context, Context Maps, and the Ubiquitous Language. Using these techniques and examples, you can reduce time to market and improve quality, as you build software that is more flexible, more scalable, and more tightly aligned to business goals. Coverage includes Getting started the right way with DDD, so you can rapidly gain value from it Using DDD within diverse architectures, including Hexagonal, SOA, REST, CQRS, Event-Driven, and Fabric/Grid-Based Appropriately designing and applying Entities–and learning when to use Value Objects instead Mastering DDD’s powerful new Domain Events technique Designing Repositories for ORM, NoSQL, and other databases |
| julia programming language tutorial: Text Mining with R Julia Silge, David Robinson, 2017-06-12 Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media. Learn how to apply the tidy text format to NLP Use sentiment analysis to mine the emotional content of text Identify a document’s most important terms with frequency measurements Explore relationships and connections between words with the ggraph and widyr packages Convert back and forth between R’s tidy and non-tidy text formats Use topic modeling to classify document collections into natural groups Examine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages |
| julia programming language tutorial: Programming Entity Framework Julia Lerman, Rowan Miller, 2012 Annotation Code first is an additional means of building a model to be used with the Entity Framework and is creating a lot of excitement in the .NET development community. The reader will begin with an overview of what code first is, why it was created, how it fits into the Entity Framework and when to use it over the alternatives. |
| julia programming language tutorial: Programming with OpenSCAD Justin Gohde, Marius Kintel, 2021-08-31 Programming with OpenSCAD is a STEM-focused, learn-to-code book for beginners that introduces core computational thinking concepts through the design of 3D-printable objects. Develop coding skills as you build increasingly complex 3D models and print them into fun games, puzzles, and more. OpenSCAD is freely available open source software that enables nondesigners to easily create 3D designs using a text-based programming language. It’s a great language for beginners because the instant 3D visualization gives you immediate feedback on the results of your code. This book channels OpenSCAD’s visual benefits and user-friendliness into a STEAM-focused, project-based tutorial that teaches the basics of coding, 3D printing, and computational thinking while you develop your spatial reasoning by creating 3D designs with OpenSCAD. Presuming no prior experience with either programming or 3D design, each chapter builds a scaffolded understanding of core concepts. You’ll start by defining, drawing and displaying geometric primitives with text-based code, then expand your creative toolbox with transformation operations – like rotating, reflecting, scaling, and combining shapes. As the projects become more sophisticated, so will your programming skills; you’ll use loops for replicating objects, if statements for differentiating your designs, and parameterized, self-contained modules to divide longer scripts into separate files. Along the way, you'll learn 3D printing tips so that you can produce physical mementos of your progress and get physical feedback that lets you correct mistakes in real time. In addition, the book provides hands-on and accessible design exercises at the end of each chapter so that you can practice applying new concepts immediately after they are introduced. You’ll learn: Programming basics like working with variables, loops, conditional statements, and parameterized modules Transformation operations, such as rotate, reflect, and scale, to create complex shapes Extrusion techniques for turning 2D shapes into elaborate 3D designs Computational-thinking concepts, including decomposition, abstraction, and pattern recognition OpenSCAD’s Boolean, Minkowski and hull operations for combining multiple 3D shapes into one 3D design fundamentals, like navigating the xyz-axis, orthogonal vs. perspective views, and constructive solid geometry Organizing bigger designs into separate files to make code more readable and collaborative Accessibly written for a wide audience (advanced middle schoolers, high school students, college students, artists, makers and lifelong-learners alike), this is the perfect guide to becoming proficient at programming in general and 3D modeling in particular. |
| julia programming language tutorial: Using R for Introductory Econometrics Florian Heiss, 2020-05-24 Introduces the popular, powerful and free programming language and software package R Focus implementation of standard tools and methods used in econometrics Compatible with Introductory Econometrics by Jeffrey M. Wooldridge in terms of topics, organization, terminology and notation Companion website with full text, all code for download and other goodies: http: //urfie.net Also check out Using Python for Introductory Econometrics http: //upfie.net/ Praise A very nice resource for those wanting to use R in their introductory econometrics courses. (Jeffrey M. Wooldridge) Using R for Introductory Econometrics is a fabulous modern resource. I know I'm going to be using it with my students, and I recommend it to anyone who wants to learn about econometrics and R at the same time. (David E. Giles in his blog Econometrics Beat) Topics: A gentle introduction to R Simple and multiple regression in matrix form and using black box routines Inference in small samples and asymptotics Monte Carlo simulations Heteroscedasticity Time series regression Pooled cross-sections and panel data Instrumental variables and two-stage least squares Simultaneous equation models Limited dependent variables: binary, count data, censoring, truncation, and sample selection Formatted reports and research papers combining R with R Markdown or LaTeX |
| julia programming language tutorial: Tiny C Projects Dan Gookin, 2023-01-24 Learn the big skills of C programming by creating bite-size projects! Work your way through these 15 fun and interesting tiny challenges to master essential C techniques you’ll use in full-size applications. In Tiny C Projects you will learn how to: Create libraries of functions for handy use and re-use Process input through an I/O filter to generate customized output Use recursion to explore a directory tree and find duplicate files Develop AI for playing simple games Explore programming capabilities beyond the standard C library functions Evaluate and grow the potential of your programs Improve code to better serve users Tiny C Projects is an engaging collection of 15 small programming challenges! This fun read develops your C abilities with lighthearted games like tic-tac-toe, utilities like a useful calendar, and thought-provoking exercises like encoding and cyphers. Jokes and lighthearted humor make even complex ideas fun to learn. Each project is small enough to complete in a weekend, and encourages you to evolve your code, add new functions, and explore the full capabilities of C. About the technology The best way to gain programming skills is through hands-on projects—this book offers 15 of them. C is required knowledge for systems engineers, game developers, and roboticists, and you can start writing your own C programs today. Carefully selected projects cover all the core coding skills, including storing and modifying text, reading and writing files, searching your computer’s directory system, and much more. About the book Tiny C Projects teaches C gradually, from project to project. Covering a variety of interesting cases, from timesaving tools, simple games, directory utilities, and more, each program you write starts out simple and gets more interesting as you add features. Watch your tiny projects grow into real applications and improve your C skills, step by step. What's inside Caesar cipher solver: Use an I/O filter to generate customized output Duplicate file finder: Use recursion to explore a directory tree Daily greetings: Writing the moon phase algorithm Lotto pics: Working with random numbers And 11 more fun projects! About the reader For C programmers of all skill levels. About the author Dan Gookin has over 30 years of experience writing about complex topics. His most famous work is DOS For Dummies, which established the entire For Dummies brand. Table of Contents 1 Configuration and setup 2 Daily greetings 3 NATO output 4 Caesarean cipher 5 Encoding and decoding 6 Password generators 7 String utilities 8 Unicode and wide characters 9 Hex dumper 10 Directory tree 11 File finder 12 Holiday detector 13 Calendar 14 Lotto picks 15 Tic-tac-toe |
| julia programming language tutorial: The Little Book of Julia Algorithms Ahan Sengupta, William Lau, 2021 |
| julia programming language tutorial: Low-Level Programming Igor Zhirkov, 2017-06-27 Learn Intel 64 assembly language and architecture, become proficient in C, and understand how the programs are compiled and executed down to machine instructions, enabling you to write robust, high-performance code. Low-Level Programming explains Intel 64 architecture as the result of von Neumann architecture evolution. The book teaches the latest version of the C language (C11) and assembly language from scratch. It covers the entire path from source code to program execution, including generation of ELF object files, and static and dynamic linking. Code examples and exercises are included along with the best code practices. Optimization capabilities and limits of modern compilers are examined, enabling you to balance between program readability and performance. The use of various performance-gain techniques is demonstrated, such as SSE instructions and pre-fetching. Relevant Computer Science topics such as models of computation andformal grammars are addressed, and their practical value explained. What You'll Learn Low-Level Programming teaches programmers to: Freely write in assembly language Understand the programming model of Intel 64 Write maintainable and robust code in C11 Follow the compilation process and decipher assembly listings Debug errors in compiled assembly code Use appropriate models of computation to greatly reduce program complexity Write performance-critical code Comprehend the impact of a weak memory model in multi-threaded applications Who This Book Is For Intermediate to advanced programmers and programming students |
| julia programming language tutorial: Optimization in Industry Tito A. Ciriani, Robert C. Leachman, 1994-09-06 The proceedings of an annual IBM Europe Institute held in Oberlech, Austria in August 1992 in which contributors from Europe and the US provide an overview of mathematical optimization applications in industry, considering the entire cycle of development, implementation, and operation. The 15 papers describe strategies and techniques used in various industries and recent advances in mathematical modeling and computer software with potential for industrial application. Annotation copyright by Book News, Inc., Portland, OR |
| julia programming language tutorial: Optimization Algorithms on Matrix Manifolds P.-A. Absil, R. Mahony, R. Sepulchre, 2007-12-23 Many problems in the sciences and engineering can be rephrased as optimization problems on matrix search spaces endowed with a so-called manifold structure. This book shows how to exploit the special structure of such problems to develop efficient numerical algorithms. It places careful emphasis on both the numerical formulation of the algorithm and its differential geometric abstraction--illustrating how good algorithms draw equally from the insights of differential geometry, optimization, and numerical analysis. Two more theoretical chapters provide readers with the background in differential geometry necessary to algorithmic development. In the other chapters, several well-known optimization methods such as steepest descent and conjugate gradients are generalized to abstract manifolds. The book provides a generic development of each of these methods, building upon the material of the geometric chapters. It then guides readers through the calculations that turn these geometrically formulated methods into concrete numerical algorithms. The state-of-the-art algorithms given as examples are competitive with the best existing algorithms for a selection of eigenspace problems in numerical linear algebra. Optimization Algorithms on Matrix Manifolds offers techniques with broad applications in linear algebra, signal processing, data mining, computer vision, and statistical analysis. It can serve as a graduate-level textbook and will be of interest to applied mathematicians, engineers, and computer scientists. |
| julia programming language tutorial: Seven Languages in Seven Weeks Bruce Tate, 2010 Seven Languages in Seven Weeks presents a meaningful exploration of seven languages within a single book. Rather than serve as a complete reference or installation guide, the book hits what's essential and unique about each language. |
| julia programming language tutorial: Jess in Action Ernest Friedman-Hill, 2003 |
The Julia Programming Language
The official website for the Julia Language. Julia is a language that is fast, dynamic, easy to use, and open source. Click here to learn more.
Julia Documentation · The Julia Language
Julia provides ease and expressiveness for high-level numerical computing, in the same way as languages such as R, MATLAB, and Python, but also supports general programming.
JuliaLang/julia: The Julia Programming Language - GitHub
Julia is a high-level, high-performance dynamic language for technical computing. The main homepage for Julia can be found at julialang.org. This is the GitHub repository of Julia source …
Julia Language Introduction - GeeksforGeeks
Apr 16, 2020 · Julia is a dynamic, high-performance programming language that is used to perform operations in scientific computing. Similar to R Programming Language, Julia is used …
Julia (programming language) - Wikipedia
Julia is a high-level, general-purpose [17] dynamic programming language, designed to be fast and productive, [18] for e.g. data science, artificial intelligence, machine learning, modeling …
Introduction to Julia - JuliaAcademy
This course curriculum is designed with the objective of enabling beginner level programmers in getting up to speed with Julia
Download Julia
The official website for the Julia Language. Julia is a language that is fast, dynamic, easy to use, and open source. Click here to learn more.
Julia Lab: Language, Composability, and Scientific Machine ...
Climate change refers to long-term shifts in temperatures and weather patterns. Human activities have been the main driver of climate change, primarily due to the burning of fossil fuels like …
The Julia Programming Language: Uses, Benefits, and Careers
Sep 27, 2024 · Julia is an open-source project known for its high performance and ability to generate highly efficient code at speeds approaching those of languages like C and Fortran. A …
Getting Started · The Julia Language
The easiest way to learn and experiment with Julia is by starting an interactive session (also known as a read-eval-print loop or "REPL") by double-clicking the Julia executable or running …
The Julia Programming Language
The official website for the Julia Language. Julia is a language that is fast, dynamic, easy to use, and open source. Click here to learn more.
Julia Documentation · The Julia Language
Julia provides ease and expressiveness for high-level numerical computing, in the same way as languages such as R, MATLAB, and Python, but also …
JuliaLang/julia: The Julia Programming Language - Git…
Julia is a high-level, high-performance dynamic language for technical computing. The main homepage for Julia can be found at julialang.org. …
Julia Language Introduction - GeeksforGeeks
Apr 16, 2020 · Julia is a dynamic, high-performance programming language that is used to perform operations in scientific computing. Similar to R …
Julia (programming language) - Wikipedia
Julia is a high-level, general-purpose [17] dynamic programming language, designed to be fast and productive, [18] for e.g. data science, artificial …
