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Journey of the Mind: How Thinking Emerged from Chaos
by Sai Gaddam Ogi OgasTwo neuroscientists reveal why consciousness exists and how it works by examining eighteen increasingly intelligent minds, from microbes to humankind—and beyond. Why do you exist? How did atoms and molecules transform into sentient creatures that experience longing, regret, compassion, and even marvel at their own existence? What does it truly mean to have a mind—to think? Science has offered few answers to these existential questions until now. Journey of the Mind is the first book to offer a unified account of the mind that explains how consciousness, language, self-awareness, and civilization arose incrementally out of chaos. The journey begins three billion years ago with the emergence of the universe’s simplest possible mind. From there, the book explores the nanoscopic archaeon, whose thinking machinery consists of a handful of molecules, then advances through amoebas, worms, frogs, birds, monkeys, and humans, explaining what each “new” mind could do that previous minds could not. Though they admire the triumph of human consciousness, Ogi Ogas and Sai Gaddam argue that humans are hardly the most sophisticated minds on the planet. The same physical principles that produce human self-awareness are leading cities and nation-states to develop “superminds,” and perhaps planting the seeds for even higher forms of consciousness. Written in lively, accessible language accompanied by vivid illustrations, Journey of the Mind is a mind-bending work of popular science, the first general book to share the cutting-edge mathematical basis for consciousness, language, and the self. It shows how a “unified theory of the mind” can explain the mind’s greatest mysteries—and offer clues about the ultimate fate of all minds in the universe.
Journey to Become a Google Cloud Machine Learning Engineer: Build the mind and hand of a Google Certified ML professional
by Dr. Logan SongPrepare for the GCP ML certification exam along with exploring cloud computing and machine learning concepts and gaining Google Cloud ML skillsKey FeaturesA comprehensive yet easy-to-follow Google Cloud machine learning study guideExplore full-spectrum and step-by-step practice examples to develop hands-on skillsRead through and learn from in-depth discussions of Google ML certification exam questionsBook DescriptionThis book aims to provide a study guide to learn and master machine learning in Google Cloud: to build a broad and strong knowledge base, train hands-on skills, and get certified as a Google Cloud Machine Learning Engineer. The book is for someone who has the basic Google Cloud Platform (GCP) knowledge and skills, and basic Python programming skills, and wants to learn machine learning in GCP to take their next step toward becoming a Google Cloud Certified Machine Learning professional. The book starts by laying the foundations of Google Cloud Platform and Python programming, followed the by building blocks of machine learning, then focusing on machine learning in Google Cloud, and finally ends the studying for the Google Cloud Machine Learning certification by integrating all the knowledge and skills together. The book is based on the graduate courses the author has been teaching at the University of Texas at Dallas. When going through the chapters, the reader is expected to study the concepts, complete the exercises, understand and practice the labs in the appendices, and study each exam question thoroughly. Then, at the end of the learning journey, you can expect to harvest the knowledge, skills, and a certificate.What you will learnProvision Google Cloud services related to data science and machine learningProgram with the Python programming language and data science librariesUnderstand machine learning concepts and model development processesExplore deep learning concepts and neural networksBuild, train, and deploy ML models with Google BigQuery ML, Keras, and Google Cloud Vertex AIDiscover the Google Cloud ML Application Programming Interface (API)Prepare to achieve Google Cloud Professional Machine Learning Engineer certificationWho this book is forAnyone from the cloud computing, data analytics, and machine learning domains, such as cloud engineers, data scientists, data engineers, ML practitioners, and engineers, will be able to acquire the knowledge and skills and achieve the Google Cloud professional ML Engineer certification with this study guide. Basic knowledge of Google Cloud Platform and Python programming is required to get the most out of this book.
Journey to the End: Secrets of an Overworld Survivor (Secrets of an Overworld Survivor #6)
by Grace Sandford Greyson MannWhen Will discovers a tattered pair of Elytra wings, he’s determined to repair them. Will’s almost ready to soar toward his next adventure when he realizes something: unless he can find a second set of wings, Mina won’t be able to come with him! There’s only one place to look for Elytra wings for his best friend—and only one way to get there. To get to the End, Will and Mina will have to battle plenty of Endermen. And win. Once there, they’ll have to take on the shulkers that guard the End Cities and End Ships of the outer islands. And when Will and Mina come face to face with the fierce Ender Dragon, they'll need more than a potion of luck to get those Elytra wings—and to make it back home alive!
Journeys to Data Mining
by Mohamed Medhat GaberData mining, an interdisciplinary field combining methods from artificial intelligence, machine learning, statistics and database systems, has grown tremendously over the last 20 years and produced core results for applications like business intelligence, spatio-temporal data analysis, bioinformatics, and stream data processing. The fifteen contributors to this volume are successful and well-known data mining scientists and professionals. Although by no means an exhaustive list, all of them have helped the field to gain the reputation and importance it enjoys today, through the many valuable contributions they have made. Mohamed Medhat Gaber has asked them (and many others) to write down their journeys through the data mining field, trying to answer the following questions: 1. What are your motives for conducting research in the data mining field? 2. Describe the milestones of your research in this field. 3. What are your notable success stories? 4. How did you learn from your failures? 5. Have you encountered unexpected results? 6. What are the current research issues and challenges in your area? 7. Describe your research tools and techniques. 8. How would you advise a young researcher to make an impact? 9. What do you predict for the next two years in your area? 10. What are your expectations in the long term? In order to maintain the informal character of their contributions, they were given complete freedom as to how to organize their answers. This narrative presentation style provides PhD students and novices who are eager to find their way to successful research in data mining with valuable insights into career planning. In addition, everyone else interested in the history of computer science may be surprised about the stunning successes and possible failures computer science careers (still) have to offer.
Joyful Infographics: A Friendly, Human Approach to Data (AK Peters Visualization Series)
by Nigel HolmesIn Joyful Infographics: A Friendly, Human Approach to Data, one of the leading graphic designers of recent times shows how a judicious use of humor can make infographics more understandable. Written in non-academic, easy-to-understand language, and with historical and contemporary visual examples presented throughout, this small book provides a short history of light-hearted graphics. The text outlines nine clear ways to make graphics more understandable, explores the importance of the audience, shows you how to make information come alive during presentations through live-action ‘performance’ graphics, discusses why joy and smiling are good for you, and shows you how not to overdo it. Even if a subject is delicate, controversial, or taboo, being graphically friendly to the audience is the right way to explain it. It is the opposite of being clinically cold and just presenting the facts. If you can get readers to smile--the smile of recognition when they understand the graphic--you are more than halfway toward getting them to continue reading, and understanding, the intention of the piece. Joyful Infographics teaches you how to do just that.
Joystick Soldiers: The Politics of Play in Military Video Games
by Nina B. Huntemann Matthew Thomas PayneJoystick Soldiers is the first anthology to examine the reciprocal relationship between militarism and video games. War has been an integral theme of the games industry since the invention of the first video game, Spacewar! in 1962.While war video games began as entertainment, military organizations soon saw their potential as combat simulation and recruitment tools. A profitable and popular relationship was established between the video game industry and the military, and continues today with video game franchises like America’s Army, which was developed by the U.S.Army as a public relations and recruitment tool. This collection features all new essays that explore how modern warfare has been represented in and influenced by video games. The contributors explore the history and political economy of video games and the "military-entertainment complex;" present textual analyses of military-themed video games such as Metal Gear Solid; and offer reception studies of gamers, fandom, and political activism within online gaming.
Jt's Conversations on True Basic: Becoming Acquainted with Basic Through Windows (2nd Edition)
by ChircoThis book started as a collection of lecture notes created for the Rutgers course 198:110 during the spring and fall of 1987. This course was, and still is, a course that introduces students to computers--culminating in a few intense weeks of learning to program in BASIC. While this publication does not contain the complete series of notes, it has a very focused purpose--concentrating on the Macintosh computer and the True BASIC programming language.
Judge This: The Terrorist's Son, The Mathematics Of Love, The Art Of Stillness, The Future Of Architecture, Beyond Measure, Judge This, How We'll Live On Mars, Why We Work, The Laws Of Medicine, And Follow Your Gut (TED Books)
by Chip KiddA fun, playful look at the importance of first impressions--in design and in life--from acclaimed book designer Chip Kidd.First impressions are everything. They dictate whether something stands out, how we engage with it, whether we buy it, and how we feel. In Judge This, renowned designer Chip Kidd takes us through his day as he takes in first impressions of all kinds. We follow this visual journey as Kidd encounters and engages with everyday design, breaking down the good, the bad, the absurd, and the brilliant as only someone with a critical, trained eye can. From the design of your morning paper to the subway ticket machine to the books you browse to the smartphone you use to the packaging for the chocolate bar you buy as an afternoon treat, Kidd reveals the hidden secrets behind each of the design choices, with a healthy dose of humor, expertise, and of course, judgment as he goes. Judge This is a design love story, exposing the often invisible beauty and betrayal in simple design choices--ones most of us never even think to notice. And with each object, Kidd proves that first impressions, whether we realize it or not, have a huge impact on the way we perceive the world.
Judgement-Proof Robots and Artificial Intelligence: A Comparative Law and Economics Approach
by Mitja KovačThis book addresses the role of public policy in regulating the autonomous artificial intelligence and related civil liability for damage caused by the robots (and any form of artificial intelligence). It is a very timely book, focusing on the consequences of judgment proofness of autonomous decision-making on tort law, risk and safety regulation, and the incentives stemming from these. This book is extremely important as regulatory endeavours concerning AI are in their infancy at most, whereas the industry’s development is continuing in a strong way. It is an important scientific contribution that will bring scientific objectivity to a, to date, very one-sided academic treatment of legal scholarship on AI.
Judicial Anti-Corruption Campaigns: Prosecutions in Italy, Brazil and Romania (Routledge Corruption and Anti-Corruption Studies)
by Nedim HogicThis book offers a comparative analysis of cases of mass prosecutions for political corruption in Italy, Brazil, and Romania.The book outlines how judicial showdowns with political corruption emerge, what consequences they create, and whether they can be considered legitimate judicial operations or coups orchestrated by political forces with the assistance of the judiciary. Due to the similarities exhibited in the legal, political, and economic spheres of the events, the book explores the Italian Mani Pulite, Brazilian Lava Jato, and Romanian judicial anti-corruption campaigns. These campaigns were simultaneously legal operations, narratives created by the media, and opportunities for the emergence of new political actors. The book demonstrates why judicial activity was a crucial tool for reducing and preventing corruption in these countries. In addition to investigating the key legal issues and narratives, the book explores the controversies of these campaigns and assesses whether they should be seen as judicial revolutions or cases of prosecutorial overreach.Providing a holistic comparative assessment of judicial means as a tool for controlling corruption, this book will be of interest to academics in the fields of Corruption Studies, Law, Development Studies, and Political Science.
Juego de Angry Birds Epic Wiki, Trucos, Armería, Descarga la Guía no Oficial
by Joshua Abbott Emmanuel CastroJuego de Angry Birds Epic Wiki, Trucos, Armería, Descarga la Guía no Oficial. Una vez que comienzas a implementar estrategias, expuestas en esta guía, tendrás muchas oportunidades de éxito. Además, te encontrarás ganando todos los niveles en los que estabas atorado y serás capaz de intentarlos vez tras vez con el truco de vidas ilimitadas. Esto no solo hará el juego mucho más interesante y disfrutable sino que también te darás cuenta del mayor nivel de logro. ¡Buena suerte!
Juegos y videojuegos: Un escape interactivo de la monotonía... (¿Cómo...? #95)
by Owen Jones¿Por qué a la gente le gusta jugar? Bueno, muchos millones de profesionales y padres preocupados se han estado haciendo esta misma pregunta durante al menos cincuenta años. Cuando los videojuegos comenzaron en los años setenta, la mayoría de la gente los veía como un poco de diversión, pero a medida que la locura se afianzaba, incluso en esa década, los intelectuales y educadores comenzaron a llamarlo “una estúpida distracción” y a los jugadores “tontos”. Esto continuó hasta hace relativamente poco tiempo, y se escribieron millones de palabras despectivas sobre los videojuegos y los jugadores. Sin embargo, la marea ahora está cambiando, y los ‘expertos’ están pontificando sobre los aspectos positivos de los juegos para los jugadores… incluso para los más jóvenes. Mientras este debate se arremolinaba a su alrededor, los jugadores seguían con lo que más les gustaba hacer: jugar videojuegos. Mucho se ha escrito acerca de por qué los juegos conquistaron a la juventud de los setenta y por qué ahora, a personas de todas las edades les encanta jugar. A algunos les gusta el juego de roles, a otros les gusta la asunción de riesgos virtuales y a otros les gusta perfeccionar habilidades que normalmente no usarían. Algunos incluso sueñan con unirse a las delgadas filas de los jugadores de élite millonarios. Sea cual sea el motivo, ¡que disfrutes mucho tiempo de tu afición y que la Fuerza te acompañe! La información en este libro electrónico sobre varios tipos de juegos, video, computadora, arcade y temas relacionados está organizada en 16 capítulos de aproximadamente 500 a 600 palabras cada uno. Como beneficio adicional, le doy permiso para utilizar el contenido en su propio sitio web o en sus propios blogs y boletines, aunque es mejor si primero los vuelve a escribir con sus propias palabras.
Jugendliche und die Aneignung politischer Information in Online-Medien
by Ulrike Wagner Christa GebelOnline-Medien eröffnen Jugendlichen in Hinblick auf politisch relevante Information ein breites Spektrum an Handlungsmöglichkeiten, das sich vom Abrufen und Kommentieren aktuellster Nachrichten über das Weiterleiten interessanter Meldungen bis zum Demoaufruf via Facebook-Posting erstreckt. Die Ergebnisse der vorliegenden Studie geben einen quantitativen Überblick, inwieweit und in welcher Intensität 12- bis 19-Jährige dieses Spektrum ausschöpfen. Darüber hinaus zeigen qualitative Fallstudien mit politisch interessierten Jugendlichen, in welchem Maße und welcher Weise sie die informationsbezogenen Handlungsmöglichkeiten für sich nutzbar machen und bewerten. Die Autorinnen diskutieren die Ergebnisse in Hinblick auf heutige Anforderungen an die Medienkompetenz Jugendlicher unter dem Blickwinkel der Mediatisierung gesellschaftlicher Partizipation.
Julia 1.0 Programming Complete Reference Guide: Discover Julia, a high-performance language for technical computing
by Ivo Balbaert Adrian SalceanuLearn dynamic programming with Julia to build apps for data analysis, visualization, machine learning, and the webKey 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 DescriptionJulia 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 forIf 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.
Julia 1.0 Programming Cookbook: Over 100 Numerical And Distributed Computing Recipes For Your Daily Data Science Workflow
by Przemyslaw Szufel Bogumil KaminskiThis 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.
Julia 1.0 Programming: Dynamic and high-performance programming to build fast scientific applications, 2nd Edition
by Ivo BalbaertEnter the exciting world of Julia, a high-performance language for technical computingKey FeaturesLeverage Julia's high speed and efficiency for your applicationsWork with Julia in a multi-core, distributed, and networked environmentApply Julia to tackle problems concurrently and in a distributed environmentBook DescriptionThe release of Julia 1.0 is now ready to change the technical world by combining the high productivity and ease of use of Python and R with the lightning-fast speed of C++. Julia 1.0 programming gives you a head start in tackling your numerical and data problems. You will begin by learning how to set up a running Julia platform, before exploring its various built-in types. With the help of practical examples, this book walks you through two important collection types: arrays and matrices. In addition to this, you will be taken through how type conversions and promotions work. In the course of the book, you will be introduced to the homo-iconicity and metaprogramming concepts in Julia. You will understand how Julia provides different ways to interact with an operating system, as well as other languages, and then you'll discover what macros are. Once you have grasped the basics, you’ll study what makes Julia suitable for numerical and scientific computing, and learn about the features provided by Julia. By the end of this book, you will also have learned how to run external programs. This book covers all you need to know about Julia in order to leverage its high speed and efficiency for your applications.What you will learnSet up your Julia environment to achieve high productivityCreate 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 debugging, among other usesApply Julia to tackle problems concurrentlyIntegrate Julia with other languages such as C, Python, and MATLABWho this book is forJulia 1.0 Programming is for you if you are a statistician or data scientist who wants a crash course in the Julia programming language while building big data applications. A basic knowledge of mathematics is needed to understand the various methods that are used or created during the course of the book to exploit the capabilities that Julia is designed with.
Julia Cookbook
by Jalem Raj RohitOver 40 recipes to get you up and running with programming using Julia About This Book * Follow a practical approach to learn Julia programming the easy way * Get an extensive coverage of Julia's packages for statistical analysis * This recipe-based approach will help you get familiar with the key concepts in Juli Who This Book Is For This book is for data scientists and data analysts who are familiar with the basics of the Julia language. Prior experience of working with high-level languages such as MATLAB, Python, R, or Ruby is expected. What You Will Learn * Extract and handle your data with Julia * Uncover the concepts of metaprogramming in Julia * Conduct statistical analysis with StatsBase.jl and Distributions.jl * Build your data science models * Find out how to visualize your data with Gadfly * Explore big data concepts in Julia In Detail Want to handle everything that Julia can throw at you and get the most of it every day? This practical guide to programming with Julia for performing numerical computation will make you more productive and able work with data more efficiently. The book starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We'll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation. Later on, you'll see how to optimize data science programs with parallel computing and memory allocation. You'll get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform. This book includes recipes on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the book, you will acquire the skills to work more effectively with your data. Style and approach This book has a recipe-based approach to help you grasp the concepts of Julia programming.
Julia High Performance: Optimizations, distributed computing, multithreading, and GPU programming with Julia 1.0 and beyond, 2nd Edition
by Avik SenguptaDesign and develop high-performance programs in Julia 1.0Key FeaturesLearn the characteristics of high-performance Julia codeUse the power of the GPU to write efficient numerical codeSpeed up your computation with the help of newly introduced shared memory multi-threading in Julia 1.0Book DescriptionJulia is a high-level, high-performance dynamic programming language for numerical computing. If you want to understand how to avoid bottlenecks and design your programs for the highest possible performance, then this book is for you. The book starts with how Julia uses type information to achieve its performance goals, and how to use multiple dispatches to help the compiler emit high-performance machine code. After that, you will learn how to analyze Julia programs and identify issues with time and memory consumption. We teach you how to use Julia's typing facilities accurately to write high-performance code and describe how the Julia compiler uses type information to create fast machine code. Moving ahead, you'll master design constraints and learn how to use the power of the GPU in your Julia code and compile Julia code directly to the GPU. Then, you'll learn how tasks and asynchronous IO help you create responsive programs and how to use shared memory multithreading in Julia. Toward the end, you will get a flavor of Julia's distributed computing capabilities and how to run Julia programs on a large distributed cluster.By the end of this book, you will have the ability to build large-scale, high-performance Julia applications, design systems with a focus on speed, and improve the performance of existing programs.What you will learnUnderstand how Julia code is transformed into machine codeMeasure 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 Accelerate your code via the GPUUse tasks and asynchronous IO for responsive programsRun Julia programs on large distributed clustersWho this book is forThis book is for beginners and intermediate Julia programmers who are interested in high-performance technical programming. A basic knowledge of Julia programming is assumed.
Julia High performance
by Avik SenguptaThis book is for intermediate level Julia users/developers who are interested in high performance technical computing. You must have a basic knowledge of scientific and technical computing with Julia.
Julia Programming Projects: Learn Julia 1.x by building apps for data analysis, visualization, machine learning, and the web
by Adrian SalceanuA step-by-step guide that demonstrates how to build simple-to-advanced applications through examples in Julia Lang 1.x using modern tools Key Features Work with powerful open-source libraries for data wrangling, analysis, and visualization Develop full-featured, full-stack web applications Learn to perform supervised and unsupervised machine learning and time series analysis with Julia Book 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 learn Leverage Julia's strengths, its top packages, and main IDE options Analyze and manipulate datasets using Julia and DataFrames Write complex code while building real-life Julia applications Develop and run a web app using Julia and the HTTP package Build a recommender system using supervised machine learning Perform exploratory data analysis Apply unsupervised machine learning algorithms Perform time series data analysis, visualization, and forecasting Who 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 Programming for Physics Applications
by R. Gökhan Türeci Hamdi Dağıstanlı İlkay Türk ÇakırNavigating the realm where physics intersects with programming, this book serves as an indispensable guide for students embarking on their journey with Julia. Whether it is plotting equations or analyzing experimental data, mastering computational tools is essential for unraveling the complexities of physical phenomena. Julia, an open-source programming language, emerges as the bridge between simplicity and efficiency. While Python, another open-source language, offers user-friendly syntax, its line-by-line execution often leads to sluggish performance. Julia, however, embodies the ethos of being "as easy as Python but as fast as C/C++," tailored specifically for scientific computing with ongoing developmental enhancements. Notably, Microsoft's AI assistant Copilot is crafted in Julia, showcasing its versatility and adaptability. Within these pages, readers encounter cutting-edge research illustrating Julia's prowess across diverse domains. From streamlined code composition facilitated by modular architecture to the integration of artificial intelligence and graphical visualization, this book illuminates Julia's multifaceted applications. It notably avoids delving into AI algorithms, instead focusing on equipping readers with foundational Julia skills applicable to physics problem-solving. Julia boasts an extensive library ecosystem tailored for scientific computing, empowering users with tools for tasks ranging from differential equation solving to statistical analysis. Its robust support for parallel processing enables swift computations on multi-core systems, a crucial asset for handling voluminous datasets with finesse. Starting with a primer on Julia fundamentals, the book gradually transitions to practical applications across various physics subdomains. From nuclear physics to high-energy phenomena, each chapter offers hands-on exercises that cement comprehension and foster proficiency in employing computational methods to unravel complex physical phenomena. Designed as a precursor to deeper explorations into AI applications within scientific realms, this book lays the groundwork for harnessing Julia's capabilities in physics-centric contexts.
Julia Quick Syntax Reference: A Pocket Guide for Data Science Programming
by Antonello LobiancoThis 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 ForExperienced programmers new to Julia, as well as existing Julia coders new to the now stable Julia version 1.0 release.
Julia Quick Syntax Reference: A Pocket Guide for Data Science Programming
by Antonello LobiancoLearn the Julia programming language as quickly as possible. This book is a must-have reference guide that presents the essential Julia syntax in a well-organized format, updated with the latest features of Julia’s APIs, libraries, and packages. This book provides an introduction that reveals basic Julia structures and syntax; discusses data types, control flow, functions, input/output, exceptions, metaprogramming, performance, and more. Additionally, you'll learn to interface Julia with other programming languages such as R for statistics or Python. At a more applied level, you will learn how to use Julia packages for data analysis, numerical optimization, symbolic computation, and machine learning, and how to present your results in dynamic documents. The Second Edition delves deeper into modules, environments, and parallelism in Julia. It covers random numbers, reproducibility in stochastic computations, and adds a section on probabilistic analysis. Finally, it provides forward-thinking introductions to AI and machine learning workflows using BetaML, including regression, classification, clustering, and more, with practical exercises and solutions for self-learners. What You Will Learn Work with Julia types and the different containers for rapid development Use vectorized, classical loop-based code, logical operators, and blocks Explore Julia functions: arguments, return values, polymorphism, parameters, anonymous functions, and broadcasts Build custom structures in Julia Use C/C++, Python or R libraries in Julia and embed Julia in other code. Optimize performance with GPU programming, profiling and more. Manage, prepare, analyse and visualise your data with DataFrames and Plots Implement complete ML workflows with BetaML, from data coding to model evaluation, and more. Who This Book Is For Experienced programmers who are new to Julia, as well as data scientists who want to improve their analysis or try out machine learning algorithms with Julia.
Julia as a Second Language
by Erik EngheimLearn 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
Julia for Data Analysis
by Bogumil BogumilMaster 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