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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.
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
Julia for Data Science
by Anshul JoshiExplore the world of data science from scratch with Julia by your side About This Book * An in-depth exploration of Julia's growing ecosystem of packages * Work with the most powerful open-source libraries for deep learning, data wrangling, and data visualization * Learn about deep learning using Mocha.jl and give speed and high performance to data analysis on large data sets Who This Book Is For This book is aimed at data analysts and aspiring data scientists who have a basic knowledge of Julia or are completely new to it. The book also appeals to those competent in R and Python and wish to adopt Julia to improve their skills set in Data Science. It would be beneficial if the readers have a good background in statistics and computational mathematics. What You Will Learn * Apply statistical models in Julia for data-driven decisions * Understanding the process of data munging and data preparation using Julia * Explore techniques to visualize data using Julia and D3 based packages * Using Julia to create self-learning systems using cutting edge machine learning algorithms * Create supervised and unsupervised machine learning systems using Julia. Also, explore ensemble models * Build a recommendation engine in Julia * Dive into Julia's deep learning framework and build a system using Mocha.jl In Detail Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete. It is a good tool for a data science practitioner. There was a famous post at Harvard Business Review that Data Scientist is the sexiest job of the 21st century. (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century). This book will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game. This book contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. This has the practical coverage of statistics and machine learning. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations. You will then delve into the world of Deep learning in Julia and will understand the framework, Mocha.jl with which you can create artificial neural networks and implement deep learning. This book addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia. Style and approach This practical and easy-to-follow yet comprehensive guide will get you learning about Julia with respect to data science. Each topic is explained thoroughly and placed in context. For the more inquisitive, we dive deeper into the language and its use case. This is the one true guide to working with Julia in data science.
Julia: High Performance Programming
by Ivo Balbaert Malcolm Sherrington Avik SenguptaLeverage the power of Julia to design and develop high performing programs About This Book * Get to know the best techniques to create blazingly fast programs with Julia * Stand out from the crowd by developing code that runs faster than your peers' code * Complete an extensive data science project through the entire cycle from ETL to analytics and data visualization Who This Book Is For This learning path is for data scientists and for all those who work in technical and scientific computation projects. It will be great for Julia developers who are interested in high-performance technical computing. This learning path assumes that you already have some basic working knowledge of Julia's syntax and high-level dynamic languages such as MATLAB, R, Python, or Ruby. What You Will Learn * Set up your Julia environment to achieve the highest productivity * Solve your tasks in a high-level dynamic language and use types for your data only when needed * Apply Julia to tackle problems concurrently and in a distributed environment * Get a sense of the possibilities and limitations of Julia's performance * Use Julia arrays to write high performance code * Build a data science project through the entire cycle of ETL, analytics, and data visualization * Display graphics and visualizations to carry out modeling and simulation in Julia * Develop your own packages and contribute to the Julia Community In Detail In this learning path, you will learn to use an interesting and dynamic programming language--Julia! You will get a chance to tackle your numerical and data problems with Julia. You'll begin the journey by setting up a running Julia platform before exploring its various built-in types. We'll then move on to the various functions and constructs in Julia. We'll walk through the two important collection types--arrays and matrices in Julia. You will dive into how Julia uses type information to achieve its performance goals, and how to use multiple dispatch to help the compiler emit high performance machine code. You will see how Julia's design makes code fast, and you'll see its distributed computing capabilities. By the end of this learning path, you will see how data works using simple statistics and analytics, and you'll discover its high and dynamic performance--its real strength, which makes it particularly useful in highly intensive computing tasks. This learning path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: * Getting Started with Julia by Ivo Balvaert * Julia High Performance by Avik Sengupta * Mastering Julia by Malcolm Sherrington Style and approach This hands-on manual will give you great explanations of the important concepts related to Julia programming.
Jump Start Adobe XD
by Daniel SchwarzGet a Jump Start on the up and coming UX design and prototyping power tool, Experience Design! Experience Design CC (also known as XD) is a brand new design tool from Adobe. With a clean, uncluttered UI and a raft of powerful features--such as live preview, Repeat Grids, artboards, symbols and collaboration tools--XD is designed from the ground up to streamline the UX design process. It makes creating interactive, sharable prototypes a snap! This book provides a rapid and practical introduction to using Adobe XD for UX design and prototyping. You'll: Get to grips with XD's clean UI and efficient keyboard shortcutsUse XD's powerful tools, such as repeat grid and symbols, to rapidly create wireframes and prototypesCreate interactive prototypes with easeCollaborate with stakeholders using built-in sharing and feedback toolsAnd more!
Jump Start Bootstrap: Get Up to Speed With Bootstrap in a Weekend
by Syed Fazle RahmanGet a Jump Start on building applications with Bootstrap today!Originally developed by Twitter, Bootstrap is a framework that making the once-arduous process of crafting fully responsive web designs a breeze! Discover why Bootstrap is fast becoming a favorite tool of top web designers.In just one weekend with this hands-on tutorial, you'll learn how to:Integrate Bootstrap into your projectsUnderstand the basic Bootstrap templateWork with Bootstrap's gridCustomize Bootstrap to work with any project
Jump Start CSS: Get Up to Speed With CSS in a Weekend
by Louis LazarisThis short SitePoint book provides readers with a fun and yet practical introduction to Cascading Style Sheets (CSS), the language used to style and lay out all web pages. This book will quickly get you up to speed with the fundamentals of CSS and give you the confidence to start experimenting on your own. It covers: Layout techniquesImages, backgrounds, textNavigationGetting fancy with CSS3 The book is built around a real-life example project: a recipe website design. It's a fun and easily understandable project that is used to demonstrate the concepts outlined in the book in a practical way.This is a clear, approachable and very easy-to-follow book that will get you to to speed with CSS in no time.
Jump Start CoffeeScript: Get Up to Speed With CoffeeScript in a Weekend
by Earle CastledineA practical and concise introduction to CoffeeScript, a programming language that compiles into JavaScript and that makes working with JavaScript easier. The book lays out the basics of the language, its syntax, and the interesting features that set it apart from JavaScript. It should satisfy anyone with an intermediate level of understanding of JavaScript who needs a conceptual and practical introduction to CoffeeScript.The book is based around a practical project, leading you through the building of a simple HTML-based 2D game, and explaining the language's philosophy, syntax, and features step-by-step along the way. You'll be able to see results on-screen from the very beginning, starting with a simple JavaScript drawing function transformed into CoffeeScript. The game's code is based on well- understood principles and prior art, using common, simple programming patterns that are easy to grasp while also demonstrating CoffeeScript's expressiveness.This is a fun and fast-paced book that rewards you for each completed step with interesting game features. You will finish the book with the sense of accomplishment that comes from building something fun, as well as having gained a solid understanding of CoffeeScript and an interest in exploring its more advanced features.
Jump Start Foundation: Get Up to Speed With Foundation in a Weekend
by Syed Fazle Rahman Joe HewitsonGet a Jump Start on building applications with Foundation today! Developed by Zurb, Foundation is a hugely popular CSS framework that's making the once-arduous process of crafting responsive web designs a breeze! Foundation was the first open-source front-end framework to be responsive, the first to be semantic, as well as the first to take a mobile-first approach. Read this book and discover why Foundation is fast becoming a favorite tool of top web designers. In just one weekend with this hands-on tutorial, you'll learn how to: Integrate Foundation into your projectsUnderstand the basic Foundation templateUtilize Foundation's impressive array of built-in componentsWork with Foundation's gridCustomize Foundation to work with any projectAnd much more!
Jump Start Git: Take Control of Your Code and Assets
by Shaumik DaityariGet a Jump Start on version control with Git today! Most engineers we meet prefer Git over other distributed version control systems. These systems let you store different versions of project files and directories, so you can roll back to an earlier one if something goes wrong. And since they're distributed, they smooth the path for dev team collaboration. But what is it about Git that makes it mission-critical on so many web projects? Spend just one weekend with this hands-on tutorial, and you'll know the answer. Understand Git's core philosophy.Get started with Git: install it, learn the basic commands, and set up your first project.Work with Git as part of a collaborative team.Use Git's debugging tools for maximum debug efficiency.Take control with Git's advanced features: reflog, rebase, stash, and more.Use Git with cloud-based Git repository host services like Github and Bitbucket.See how Git's used effectively on large open-source projects. Git was created by Linus Torvalds, who invented Linux, so it comes with the best credentials. What are you waiting for? Get a jump start on Git today.
Jump Start HTML5: Get Up to Speed With HTML5 in a Weekend
by Sandeep Panda Tiffany B Brown Kerry ButtersThis short book provides a practical introduction to HTML5. HTML (HyperText Markup Language) is the predominant language of web pages. Originally developed as a way to describe and share scientific papers, HTML is now used to mark up all sorts of documents and create visual interfaces for browser-based software. With HTML5, however, HTML has become as much an of API for developing browser-based software as it is a markup language. In this book, we'll talk about the history of HTML and HTML5 and explore its new features. It covers: HTML5 basicsMultimediaCanvas and SVGHTML5 applicationsHTML5 APIs HTML5 is required knowledge for every professional web designer and developer. This book will quickly get you up to speed with the fundamentals of HTML5 and give you the confidence to start experimenting on your own.