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A powerful and compelling story which explores one of the most difficult decisions we might ever have to make. One morning in October, William Harris is confronted by the shocking disappearance of the woman he loves. Julia Seymour has vanished without trace - from his life, from her daughter's and from her own. Her sudden departure seems to be both deliberate and final. But William is determined to find her. In the days that follow, he tries to piece together what might have driven her away. His search takes him to London, to India - and to Julia's life before he met her. In the process, William discovers secrets about Julia's past that challenge and disturb his view of all they shared together. Secrets that illuminate the present in ways he could never have expected. Praise for Catherine Dunne 'A real touch of Jodi Picoult . . . a domestic setting . . . tension . . . and a little bit of darkness' Arena Arts Review, RTÉ Radio 1
Master core data analysis skills using Julia. Interesting hands-on projects guide you through time series data, predictive models, popularity ranking, and more. In Julia for Data Analysis you will learn how to: Read and write data in various formats Work with tabular data, including subsetting, grouping, and transforming Visualize your data Build predictive models Create data processing pipelines Create web services sharing results of data analysis Write readable and efficient Julia programs Julia was designed for the unique needs of data scientists: it's expressive and easy-to-use whilst also delivering super-fast code execution. Julia for Data Analysis shows you how to take full advantage of this amazing language to read, write, transform, analyze, and visualize data—everything you need for an effective data pipeline. It’s written by Bogumil Kaminski, one of the top contributors to Julia, #1 Julia answerer on StackOverflow, and a lead developer of Julia’s core data package DataFrames.jl. Its engaging hands-on projects get you into the action quickly. Plus, you’ll even be able to turn your new Julia skills to general purpose programming! Foreword by Viral Shah. About the technology Julia is a great language for data analysis. It’s easy to learn, fast, and it works well for everything from one-off calculations to full-on data processing pipelines. Whether you’re looking for a better way to crunch everyday business data or you’re just starting your data science journey, learning Julia will give you a valuable skill. About the book Julia for Data Analysis teaches you how to handle core data analysis tasks with the Julia programming language. You’ll start by reviewing language fundamentals as you practice techniques for data transformation, visualizations, and more. Then, you’ll master essential data analysis skills through engaging examples like examining currency exchange, interpreting time series data, and even exploring chess puzzles. Along the way, you’ll learn to easily transfer existing data pipelines to Julia. What's inside Read and write data in various formats Work with tabular data, including subsetting, grouping, and transforming Create data processing pipelines Create web services sharing results of data analysis Write readable and efficient Julia programs About the reader For data scientists familiar with Python or R. No experience with Julia required. About the author Bogumil Kaminski iis one of the lead developers of DataFrames.jl—the core package for data manipulation in the Julia ecosystem. He has over 20 years of experience delivering data science projects. Table of Contents 1 Introduction PART 1 ESSENTIAL JULIA SKILLS 2 Getting started with Julia 3 Julia’s support for scaling projects 4 Working with collections in Julia 5 Advanced topics on handling collections 6 Working with strings 7 Handling time-series data and missing values PART 2 TOOLBOX FOR DATA ANALYSIS 8 First steps with data frames 9 Getting data from a data frame 10 Creating data frame objects 11 Converting and grouping data frames 12 Mutating and transforming data frames 13 Advanced transformations of data frames 14 Creating web services for sharing data analysis results
Learn the awesome Julia programming language by building fun projects like a rocket launcher, a password keeper, and a battle simulator. Julia as a Second Language covers: 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 Julia as a Second Language 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. About the Technology Originally designed for high-performance data science, Julia has become an awesome general purpose programming language. It offers developer-friendly features like garbage collection, dynamic typing, and a flexible approach to concurrency and distributed computing. It is the perfect mix of simplicity, flexibility and performance. About the Book Julia as a Second Language introduces Julia by building on your existing programming knowledge. You’ll see Julia in action as you create a series of interesting projects that guide you from Julia’s basic syntax through its advanced features. Master types and data structures as you model a rocket launch. Use dictionaries to interpret Roman numerals. Use Julia’s unique multiple dispatch feature to send knights and archers into a simulated battle. Along the way, you’ll even compare the object-oriented and functional programming styles–Julia supports both! What’s Inside Data types like numbers, strings, arrays, and dictionaries Immediate feedback with Julia’s read-evaluate-print-loop (REPL) Simplify code interactions with multiple dispatch Share code using modules and packages About the Reader For readers comfortable with another programming language like Python, JavaScript, or C#. About the Author Erik Engheim is a writer, conference speaker, video course author, and software developer. Table of Contents PART 1 - BASICS 1 Why Julia? 2 Julia as a calculator 3 Control flow 4 Julia as a spreadsheet 5 Working with text 6 Storing data in dictionaries PART 2 - TYPES 7 Understanding types 8 Building a rocket 9 Conversion and promotion 10 Representing unknown values PART 3 - COLLECTIONS 11 Working with strings 12 Understanding Julia collections 13 Working with sets 14 Working with vectors and matrices PART 4 - SOFTWARE ENGINEERING 15 Functional programming in Julia 16 Organizing and modularizing your code PART 5 - GOING IN DEPTH 17 Input and output 18 Defining parametric types
Learn to use Julia as a tool for research, and solve problems of genuine interest—like modeling the course of a pandemic—in this practical, hands-on introduction to the language. The Julia programming language is acclaimed in scientific circles for its unparalleled ease, interactivity, and speed. Practical Julia is a comprehensive introduction to the language, making it accessible even if you’re new to programming. 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 Whether your interest is in scientific research, statistics, mathematics, or just the fun of programming with Julia, Practical Julia will have you writing high-performance code that can do real work in no time. Online Resources: Ready-to-run code samples, illustrations, and supplemental animations available at https://julia.lee-phillips.org.
Julia 1.0 Programming Cookbook by Bogumił Kamiński,Przemysław Szufel Pdf
Discover the new features and widely used packages in Julia to solve complex computational problems in your statistical applications. Key FeaturesAddress the core problems of programming in Julia with the most popular packages for common tasksTackle issues while working with Databases and Parallel data processing with JuliaExplore advanced features such as metaprogramming, functional programming, and user defined typesBook Description Julia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia. Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia. By the end of the book, you will have acquired the skills to work more effectively with your data What you will learnBoost your code’s performance using Julia’s unique featuresOrganize data in to fundamental types of collections: arrays and dictionariesOrganize data science processes within Julia and solve related problemsScale Julia computations with cloud computingWrite data to IO streams with Julia and handle web transferDefine your own immutable and mutable typesSpeed up the development process using metaprogrammingWho this book is for This book is for developers who would like to enhance their Julia programming skills and would like to get some quick solutions to their common programming problems. Basic Julia programming knowledge is assumed.
A step-by-step guide that demonstrates how to build simple-to-advanced applications through examples in Julia Lang 1.x using modern tools Key FeaturesWork with powerful open-source libraries for data wrangling, analysis, and visualizationDevelop full-featured, full-stack web applications Learn to perform supervised and unsupervised machine learning and time series analysis with JuliaBook Description Julia is a new programming language that offers a unique combination of performance and productivity. Its powerful features, friendly syntax, and speed are attracting a growing number of adopters from Python, R, and Matlab, effectively raising the bar for modern general and scientific computing. After six years in the making, Julia has reached version 1.0. Now is the perfect time to learn it, due to its large-scale adoption across a wide range of domains, including fintech, biotech, education, and AI. Beginning with an introduction to the language, Julia Programming Projects goes on to illustrate how to analyze the Iris dataset using DataFrames. You will explore functions and the type system, methods, and multiple dispatch while building a web scraper and a web app. Next, you'll delve into machine learning, where you'll build a books recommender system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco business database. After metaprogramming, the final chapters will discuss dates and time, time series analysis, visualization, and forecasting. We'll close with package development, documenting, testing and benchmarking. By the end of the book, you will have gained the practical knowledge to build real-world applications in Julia. What you will learnLeverage Julia's strengths, its top packages, and main IDE optionsAnalyze and manipulate datasets using Julia and DataFramesWrite complex code while building real-life Julia applicationsDevelop and run a web app using Julia and the HTTP packageBuild a recommender system using supervised machine learning Perform exploratory data analysis Apply unsupervised machine learning algorithmsPerform time series data analysis, visualization, and forecastingWho this book is for Data scientists, statisticians, business analysts, and developers who are interested in learning how to use Julia to crunch numbers, analyze data and build apps will find this book useful. A basic knowledge of programming is assumed.
Julia 1.0 Programming Complete Reference Guide by Ivo Balbaert,Adrian Salceanu Pdf
Learn dynamic programming with Julia to build apps for data analysis, visualization, machine learning, and the web Key FeaturesLeverage Julia's high speed and efficiency to build fast, efficient applicationsPerform supervised and unsupervised machine learning and time series analysisTackle problems concurrently and in a distributed environmentBook Description Julia offers the high productivity and ease of use of Python and R with the lightning-fast speed of C++. There’s never been a better time to learn this language, thanks to its large-scale adoption across a wide range of domains, including fintech, biotech and artificial intelligence (AI). You will begin by learning how to set up a running Julia platform, before exploring its various built-in types. This Learning Path walks you through two important collection types: arrays and matrices. You’ll be taken through how type conversions and promotions work, and in further chapters you'll study how Julia interacts with operating systems and other languages. You’ll also learn about the use of macros, what makes Julia suitable for numerical and scientific computing, and how to run external programs. Once you have grasped the basics, this Learning Path goes on to how to analyze the Iris dataset using DataFrames. While building a web scraper and a web app, you’ll explore the use of functions, methods, and multiple dispatches. In the final chapters, you'll delve into machine learning, where you'll build a book recommender system. By the end of this Learning Path, you’ll be well versed with Julia and have the skills you need to leverage its high speed and efficiency for your applications. This Learning Path includes content from the following Packt products: Julia 1.0 Programming - Second Edition by Ivo BalbaertJulia Programming Projects by Adrian SalceanuWhat you will learnCreate your own types to extend the built-in type systemVisualize your data in Julia with plotting packagesExplore the use of built-in macros for testing and debuggingIntegrate Julia with other languages such as C, Python, and MATLABAnalyze and manipulate datasets using Julia and DataFramesDevelop and run a web app using Julia and the HTTP packageBuild a recommendation system using supervised machine learningWho this book is for If you are a statistician or data scientist who wants a quick course in the Julia programming language while building big data applications, this Learning Path is for you. Basic knowledge of mathematics and programming is a must.
From the critically acclaimed author of Invisible City and Conviction, The Missing Hours is a novel about obsession, privilege, and the explosive consequences of one violent act. From a distance, Claudia Castro has it all: a famous family, a trust fund, thousands of Instagram followers, and a spot in NYU’s freshman class. But look closer, and things are messier: her parents are separating, she’s just been humiliated by a sleazy documentary, and her sister is about to have a baby with a man she barely knows. Claudia starts the school year resolved to find a path toward something positive, maybe even meaningful – and then one drunken night everything changes. Reeling, her memory hazy, Claudia cuts herself off from her family, seeking solace in a new friendship. But when the rest of school comes back from spring break, Claudia is missing. Suddenly, the whole city is trying to piece together the hours of that terrible night.
Ultimate Parallel and Distributed Computing with Julia For Data Science by Nabanita Dash Pdf
Unleash Julia’s power: Code Your Data Stories, Shape Machine Intelligence! KEY FEATURES ● Comprehensive Learning Journey from fundamentals of Julia ML to advanced techniques. ● Immersive practical approach with real-world examples, exercises, and scenarios, ensuring immediate application of acquired knowledge. ● Delve into the unique features of Julia and unlock its true potential to excel in modern ML applications. DESCRIPTION This book takes you through a step-by-step learning journey, starting with the essentials of Julia's syntax, variables, and functions. You'll unlock the power of efficient data handling by leveraging Julia arrays and DataFrames.jl for insightful analysis. Develop expertise in both basic and advanced statistical models, providing a robust toolkit for deriving meaningful data-driven insights. The journey continues with machine learning proficiency, where you'll implement algorithms confidently using MLJ.jl and MLBase.jl, paving the way for advanced data-driven solutions. Explore the realm of Bayesian inference skills through practical applications using Turing.jl, enhancing your ability to extract valuable insights. The book also introduces crucial Julia packages such as Plots.jl for visualizing data and results. The handbook culminates in optimizing workflows with Julia's parallel and distributed computing capabilities, ensuring efficient and scalable data processing using Distributions.jl, Distributed.jl and SharedArrays.jl. This comprehensive guide equips you with the knowledge and practical insights needed to excel in the dynamic field of data science and machine learning. WHAT WILL YOU LEARN ● Master Julia ML Basics to gain a deep understanding of Julia's syntax, variables, and functions. ● Efficient Data Handling with Julia arrays and DataFrames for streamlined and insightful analysis. ● Develop expertise in both basic and advanced statistical models for informed decision-making through Statistical Modeling. ● Achieve Machine Learning Proficiency by confidently implementing ML algorithms using MLJ.jl and MLBase.jl. ● Apply Bayesian Inference Skills with Turing.jl for advanced modeling techniques. ● Optimize workflows using Julia's Parallel Processing Capabilities and Distributed Computing for efficient and scalable data processing. WHO IS THIS BOOK FOR? This book is designed to be a comprehensive and accessible companion for anyone eager to excel in machine learning and data analysis using Julia. Whether you are a novice or an experienced practitioner, the knowledge and skills imparted within these pages will empower you to navigate the complexities of modern data science with Julia. TABLE OF CONTENTS 1. Julia In Data Science Arena 2. Getting Started with Julia 3. Features Assisting Scaling ML Projects 4. Data Structures in Julia 5. Working With Datasets In Julia 6. Basics of Statistics 7. Probability Data Distributions 8. Framing Data in Julia 9. Working on Data in DataFrames 10. Visualizing Data in Julia 11. Introducing Machine Learning in Julia 12. Data and Models 13. Bayesian Statistics and Modeling 14. Parallel Computation in Julia 15. Distributed Computation in Julia Index
A powerful and compelling story which explores one of the most difficult decisions we might ever have to make. One morning in October, William Harris is confronted by the shocking disappearance of the woman he loves. Julia Seymour has vanished without trace - from his life, from her daughter's and from her own. Her sudden departure seems to be both deliberate and final. But William is determined to find her. In the days that follow, he tries to piece together what might have driven her away. His search takes him to London, to India - and to Julia's life before he met her. In the process, William discovers secrets about Julia's past that challenge and disturb his view of all they shared together. Secrets that illuminate the present in ways he could never have expected. Praise for Catherine Dunne 'A real touch of Jodi Picoult . . . a domestic setting . . . tension . . . and a little bit of darkness' Arena Arts Review, RTÉ Radio 1
One of The New York Times 10 Best Books of the Year National Book Award Finalist Finalist for the National Book Critics Circle John Leonard Prize Finalist for the Center for Fiction First Novel Prize Finalist for the New York Public Library's Young Lions Fiction Award National Best Seller "Splendidly imagined . . . Thrilling" --Simon Winchester "A genuine masterpiece" --Gary Shteyngart Spellbinding, moving--evoking a fascinating region on the other side of the world--this suspenseful and haunting story announces the debut of a profoundly gifted writer. One August afternoon, on the shoreline of the Kamchatka peninsula at the northeastern edge of Russia, two girls--sisters, eight and eleven--go missing. In the ensuing weeks, then months, the police investigation turns up nothing. Echoes of the disappearance reverberate across a tightly woven community, with the fear and loss felt most deeply among its women. Taking us through a year in Kamchatka, Disappearing Earth enters with astonishing emotional acuity the worlds of a cast of richly drawn characters, all connected by the crime: a witness, a neighbor, a detective, a mother. We are transported to vistas of rugged beauty--densely wooded forests, open expanses of tundra, soaring volcanoes, and the glassy seas that border Japan and Alaska--and into a region as complex as it is alluring, where social and ethnic tensions have long simmered, and where outsiders are often the first to be accused. In a story as propulsive as it is emotionally engaging, and through a young writer's virtuosic feat of empathy and imagination, this powerful novel brings us to a new understanding of the intricate bonds of family and community, in a Russia unlike any we have seen before.
Julia Quick Syntax Reference by Antonello Lobianco Pdf
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 exampleWork with types and the different containers that Julia makes available for rapid application developmentUse vectorized, classical loop-based code, logical operators, and blocksExplore Julia functions by looking at arguments, return values, polymorphism, parameters, anonymous functions, and broadcastsBuild custom structures in JuliaInterface Julia with other languages such as C/C++, Python, and RProgram a richer API, modifying the code before it is executed using expressions, symbols, macros, quote blocks, and moreMaximize your code’s performance Who This Book Is For Experienced programmers new to Julia, as well as existing Julia coders new to the now stable Julia version 1.0 release.
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.
Statistics with Julia by Yoni Nazarathy,Hayden Klok Pdf
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.
Just Another Missing Person by Gillian McAllister Pdf
“Twisty and complex, with unexpected turns…and characters you really root for.”—Emily Henry, #1 New York Times bestselling author From the author of the Reese’s Book Club Pick and the New York Times bestseller Wrong Place Wrong Time comes a new heart-stopping thriller in which a missing-person case unravels deeper, darker secrets that lead a detective to an impossible moral choice. Not everyone who is lost should be found… Twenty-two-year-old Olivia has been missing for one day…and counting. She was last seen on CCTV, entering a dead-end alley. And not coming back out again. Julia, the detective heading up the search for Olivia, thinks she knows what to expect. A desperate family, a ticking clock, and long hours away from her husband and daughter. But she has no idea just how close to home this case is going to get. Because the criminal at the heart of the disappearance has something she never expected. His weapon isn’t a gun, or a knife: it’s a secret. Her worst one. And her family's safety depends on one thing: Julia must NOT find out what happened to Olivia - and must frame somebody else for her murder. If you find her, you will lose everything. What would you do? This clever and endlessly surprising thriller is laced with a clever look at family and motherhood, and cements Gillian McAllister as a major talent in the world of suspense and a master of creating ethical dilemmas that show just how murky the distinction between right and wrong can be.
