Verleger: Packt Publishing
Adam Boduch
jQuery UI is the quintessential framework for creating professional user interfaces. While jQuery core lays the foundation for interaction with the DOM and handling events, jQuery UI fills in the user interaction gap. This book will give you a huge productivity boost out of the box with jQuery UI, and help you understand the framework, inside and out.jQuery UI Cookbook provides you with practical recipes featuring in-depth coverage of every widget in the framework, including how to address limitations that impact your everyday development activities with these widgets. You'll get a better idea of the big picture – how the framework is composed, how the widgets relate to one another, and how to build on those patterns.Be it a minor tweak on the visual design of a progress bar or a fundamental change in a widget to meet your needs, jQuery UI Cookbook covers scenarios both big and small. You can show reminders as tooltips, apply a variety of effects to the menu widget, and start interactions between the dialog widget and API data using deferred objects. These and many more interesting tasks are covered in this book, which can be done with smooth learning and great understanding. You will see how button widgets can fill the width of their containing element, making the layout more consistent. Tabs can be sorted and moved between widgets. You will learn how to do all these things within the context of the big picture, by finding out why the components work the way they do, making you well-versed in jQuery UI.
JSF 1.2 Components. Develop advanced Ajax-enabled JSF applications
IAN HLAVATS
Today's web developers need powerful tools to deliver richer, faster, and smoother web experiences. JavaServer Faces includes powerful, feature-rich, Ajax-enabled UI components that provide all the functionality needed to build web applications in a Web 2.0 world. It's the perfect way to build rich, interactive, and Web 2.0-style Java web apps.This book provides a comprehensive introduction to the most popular JSF components available today and demonstrate step-by-step how to build increasingly sophisticated JSF user interfaces with standard JSF, Facelets, Apache Tomahawk/Trinidad, ICEfaces, JBoss Seam, JBoss RichFaces/Ajax4jsf, and JSF 2.0 components. JSF 1.2 Components is both an excellent starting point for new JSF developers, and a great reference and “how to” guide for experienced JSF professionals.This book progresses logically from an introduction to standard JSF HTML, and JSF Core components to advanced JSF UI development. As you move through the book, you will learn how to build composite views using Facelets tags, implement common web development tasks using Tomahawk components, and add Ajax capabilities to your JSF user interface with ICEfaces components. You will also learn how to solve the complex web application development challenges with the JBoss Seam framework. At the end of the book, you will be introduced to the new and up-coming JSF component libraries that will provide a road map of the future JSF technologies.
JSF 2.0 Cookbook: LITE. Converters, Validators, and Security
Anghel Leonard
Practical, hands-on “Cookbook” approach. Full of clear, step-by-step instructions that you can apply straight away. Written for people who want to get maximum results without lots of background and theory reading. JSF developers who want to work with validators, converters and security features of JSF. You don’t need any prior knowledge of JSF to use these recipes.
Ivo Balbaert, Adrian Salceanu
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 Balbaert• Julia Programming Projects by Adrian Salceanu
Bogumił Kamiński, Przemysław Szufel
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
Ivo Balbaert
The 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.
Julia Cookbook. Over 40 recipes to get you up and running with programming using Julia
Jalem Raj Rohit
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.
Julia for Data Science. high-performance computing simplified
Anshul Joshi
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.