Wydawca: Packt Publishing
Canvas Cookbook. Click here to enter text
Bhushan Purushottam Joshi, Eric Rowell
With the growing popularity of HTML5 Canvas, this book offers tailored recipes to help you develop portable applications, presentations, and games.The recipes are simple yet creative and build on each other. At every step, the book inspires the reader to develop his/her own recipe. From basic to advanced, every aspect of Canvas API has been covered to guide readers to develop their own application, presentation, or game.
Ryan John
Canvas is an online learning management system (LMS) and one of the most powerful teaching tools that educational institutions worldwide have adopted thanks to its streamlined functionality, intuitive user interface, and robust features. This revised second edition of Canvas LMS Course Design will show you how to design, create, and teach engaging classes in online, hybrid, or fusion settings using the Canvas LMS.The book will guide you through the steps of setting up your Canvas account, creating activities and assignments, designing and customizing the course layout, and teaching your course. Whether you're using an institutional or Free-for-Teacher account, you'll be able to use Canvas for grading, feedback, and communication with and between students. As you progress, you'll learn how to integrate external apps and services with Canvas. Finally, the book features an exclusive section detailing how you can use Canvas' built-in resources as well as other resources to tackle challenges while working with the LMS. Throughout, this book will give you the technical knowledge you need to create unique learning experiences for students.By the end of this Canvas LMS Course Design, you'll be able to successfully teach online with the help of logical descriptions and step-by-step screenshots that clearly explain how to build a phenomenal course.
Jonathan Linowes, Matt Schoen
Google Cardboard is a low-cost, entry-level media platform through which you can experience virtual reality and virtual 3D environments. Its applications are as broad and varied as mobile smartphone applications themselves. This book will educate you on the best practices and methodology needed to build effective, stable, and performant mobile VR applications.In this book, we begin by defining virtual reality (VR) and how Google Cardboard fits into the larger VR and Android ecosystem. We introduce the underlying scientific and technical principles behind VR, including geometry, optics, rendering, and mobile software architecture. We start with a simple example app that ensures your environment is properly set up to write, build, and run the app. Then we develop a reusable VR graphics engine that you can build upon. And from then on, each chapter is a self-contained project where you will build an example from a different genre of application, including a 360 degree photo viewer, an educational simulation of our solar system, a 3D model viewer, and a music visualizer.Given the recent updates that were rolled out at Google I/O 2016, the authors of Cardboard VR Projects for Android have collated some technical notes to help you execute the projects in this book with Google VR Cardboard Java SDK 0.8, released in May 2016. Refer to the article at https://www.packtpub.com/sites/default/files/downloads/GoogleVRUpdateGuideforCardbook.pdf which explains the updates to the source code of the projects.
Cassandra 3.x High Availability. Click here to enter text. - Second Edition
Robbie Strickland
Apache Cassandra is a massively scalable, peer-to-peer database designed for 100 percent uptime, with deployments in the tens of thousands of nodes, all supporting petabytes of data. This book offers a practical insight into building highly available, real-world applications using Apache Cassandra.The book starts with the fundamentals, helping you to understand how Apache Cassandra’s architecture allows it to achieve 100 percent uptime when other systems struggle to do so. You’ll get an excellent understanding of data distribution, replication, and Cassandra’s highly tunable consistency model. Then we take an in-depth look at Cassandra's robust support for multiple data centers, and you’ll see how to scale out a cluster. Next, the book explores the domain of application design, with chapters discussing the native driver and data modeling. Lastly, you’ll find out how to steer clear of common anti-patterns and take advantage of Cassandra’s ability to fail gracefully.
Rajanarayanan Thottuvaikkatumana
If you are new to Cassandra but well-versed in RDBMS modeling and design, then it is natural to model data in the same way in Cassandra, resulting in poorly performing applications and losing the real purpose of Cassandra. If you want to learn to make the most of Cassandra, this book is for you.This book starts with strategies to integrate Cassandra with other legacy data stores and progresses to the ways in which a migration from RDBMS to Cassandra can be accomplished. The journey continues with ideas to migrate data from cache solutions to Cassandra. With this, the stage is set and the book moves on to some of the most commonly seen problems in applications when dealing with consistency, availability, and partition tolerance guarantees. Cassandra is exceptionally good at dealing with temporal data and patterns such as the time-series pattern and log pattern, which are covered next. Many NoSQL data stores fail miserably when a huge amount of data is read for analytical purposes, but Cassandra is different in this regard. Keeping analytical needs in mind, you’ll walk through different and interesting design patterns.No theoretical discussions are complete without a good set of use cases to which the knowledge gained can be applied, so the book concludes with a set of use cases you can apply the patterns you’ve learned.
Aleksander Molak, Ajit Jaokar
Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality.You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more.By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.