A hands-on, code-based guide to leveraging Julia in a variety of scientific and data-driven scenarios Key Features Augment your basic computing skills with an in-depth introduction to Julia Focus on topic-based approaches to scientific problems and visualisation Build on prior knowledge of programming languages such as Python, R, or C/C++ Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionJulia is a well-constructed programming language which was designed for fast execution speed by using just-in-time LLVM compilation techniques, thus eliminating the classic problem of performing analysis in one language and translating it for performance in a second. This book is a primer on Julia’s approach to a wide variety of topics such as scientific computing, statistics, machine learning, simulation, graphics, and distributed computing. Starting off with a refresher on installing and running Julia on different platforms, you’ll quickly get to grips with the core concepts and delve into a discussion on how to use Julia with various code editors and interactive development environments (IDEs). As you progress, you’ll see how data works through simple statistics and analytics and discover Julia's speed, its real strength, which makes it particularly useful in highly intensive computing tasks. You’ll also and observe how Julia can cooperate with external processes to enhance graphics and data visualization. Finally, you will explore metaprogramming and learn how it adds great power to the language and establish networking and distributed computing with Julia. By the end of this book, you’ll be confident in using Julia as part of your existing skill set.What you will learn Develop simple scripts in Julia using the REPL, code editors, and web-based IDEs Get to grips with Julia’s type system, multiple dispatch, metaprogramming, and macro development Interact with data files, tables, data frames, SQL, and NoSQL databases Delve into statistical analytics, linear programming, and optimization problems Create graphics and visualizations to enhance modeling and simulation in Julia Understand Julia's main approaches to machine learning, Bayesian analysis, and AI Who this book is for This book is not an introduction to computer programming, but a practical guide for developers who want to enhance their basic knowledge of Julia, or those wishing to augment their skill set by adding Julia to their existing roster of programming languages. Familiarity with a scripting language such as Python or R, or a compiled language such as C/C++, C# or Java, is a prerequisite.

Julia is a well-constructed programming language with fast execution speed, eliminating the classic problem of performing analysis in one language and translating it for performance into a second. This book will help you develop and enhance your programming skills in Julia to solve real-world automation challenges. This book starts off with a refresher on installing and running Julia on different platforms. Next, you will compare the different ways of working with Julia and explore Julia's key features in-depth by looking at design and build. You will see how data works using simple statistics and analytics, and discover Julia's speed, its real strength, which makes it particularly useful in highly intensive computing tasks and observe how Julia can cooperate with external processes in order to enhance graphics and data visualization. Finally, you will look into meta-programming and learn how it adds great power to the language and establish networking and distributed computing with Julia.

NEW YORK TIMES BESTSELLER • The definitive cookbook on French cuisine for American readers: "What a cookbook should be: packed with sumptuous recipes, detailed instructions, and precise line drawings. Some of the instructions look daunting, but as Child herself says in the introduction, 'If you can read, you can cook.'" —Entertainment Weekly “I only wish that I had written it myself.” —James Beard Featuring 524 delicious recipes and over 100 instructive illustrations to guide readers every step of the way, Mastering the Art of French Cooking offers something for everyone, from seasoned experts to beginners who love good food and long to reproduce the savory delights of French cuisine. Julia Child, Simone Beck, and Louisette Bertholle break down the classic foods of France into a logical sequence of themes and variations rather than presenting an endless and diffuse catalogue of dishes—from historic Gallic masterpieces to the seemingly artless perfection of a dish of spring-green peas. Throughout, the focus is on key recipes that form the backbone of French cookery and lend themselves to an infinite number of elaborations—bound to increase anyone’s culinary repertoire. “Julia has slowly but surely altered our way of thinking about food. She has taken the fear out of the term ‘haute cuisine.’ She has increased gastronomic awareness a thousandfold by stressing the importance of good foundation and technique, and she has elevated our consciousness to the refined pleasures of dining." —Thomas Keller, The French Laundry

With more than 100 color photographs by Micheal McLaughlin

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.

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

This book is for you if you are a data scientist or working on any technical or scientific computation projects. The book assumes you have a basic working knowledge of high-level dynamic languages such as MATLAB, R, Python, or Ruby.