Create compelling 2D games with Learn cocos2d 2: Game Development with iOS. This book shows you how to use the powerful new cocos2d, version 2 game engine to develop games for iPhone and iPad with tilemaps, virtual joypads, Game Center, and more. It teaches you:
The process and best practices of mobile game development, including sprite batching, texture atlases, parallax scrolling, touch and accelerometer input.
How to enhance your games using the Box2D and Chipmunk physics engines and other cocos2d-related tools and libraries.
How to add UIKit views to cocos2d and how to add cocos2d to UIKit apps.
The ins and outs of the Kobold2D development environment for cocos2d and its pre-configured libraries, including cocos3d and Lua.
Best of all, this book will have you making games right from the very start. It guides you step-by-step through the creation of sample games. These fun examples are modeled after popular App Store games and teach you key concepts of the new cocos2d 2 game engine and relevant tools like TexturePacker (texture atlas), PhysicsEditor (physics collision shapes), Particle Designer (particle effects), Glyph Designer (bitmap fonts), and others.
This book offers a rock-solid introduction to creating games made entirely with cocos2d and little or no iOS SDK and OpenGL code. It also details alternative implementations, identifies the best free and commercial tools for cocos2d game development, features coverage of the author's improved cocos2d game engine (Kobold2D), and even helps you enhance your game's marketability on the App Store.
Understand the industrialization of machine learning (ML) and take the first steps toward identifying and generating the transformational disruptors of artificial intelligence (AI). You will learn to apply ML to data lakes in various industries, supplying data professionals with the advanced skills required to handle the future of data engineering and data science. Data lakes currently generated by worldwide industrialized business activities are projected to reach 35 zettabytes (ZB) as the Fourth Industrial Revolution produces an exponential increase of volume, velocity, variety, variability, veracity, visualization, and value. Industrialization of ML evolves from AI and studying pattern recognition against the increasingly unstructured resource stored in data lakes. Industrial Machine Learning supplies advanced, yet practical examples in different industries, including finance, public safety, health care, transportation, manufactory, supply chain, 3D printing, education, research, and data science. The book covers: supervised learning, unsupervised learning, reinforcement learning, evolutionary computing principles, soft robotics disruptors, and hard robotics disruptors.
What You Will Learn
Generate and identify transformational disruptors of artificial intelligence (AI)
Understand the field of machine learning (ML) and apply it to handle big data and process the data lakes in your environment
Hone the skills required to handle the future of data engineering and data science
Who This Book Is For
Intermediate to expert level professionals in the fields of data science, data engineering, machine learning, and data management
Dive into the world of SQL on Hadoop and get the most out of your Hive data warehouses. This book is your go-to resource for using Hive: authors Scott Shaw, Ankur Gupta, David Kjerrumgaard, and Andreas Francois Vermeulen take you through learning HiveQL, the SQL-like language specific to Hive, to analyze, export, and massage the data stored across your Hadoop environment. From deploying Hive on your hardware or virtual machine and setting up its initial configuration to learning how Hive interacts with Hadoop, MapReduce, Tez and other big data technologies, Practical Hive gives you a detailed treatment of the software.In addition, this book discusses the value of open source software, Hive performance tuning, and how to leverage semi-structured and unstructured data.
What You Will Learn
Install and configure Hive for new and existing datasets
Perform DDL operations
Execute efficient DML operationsUse tables, partitions, buckets, and user-defined functionsDiscover performance tuning tips and Hive best practices
Who This Book Is For
Developers, companies, and professionals who deal with large amounts of data and could use software that can efficiently manage large volumes of input. It is assumed that readers have the ability to work with SQL.
Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets.
The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions.
What You'll LearnBecome fluent in the essential concepts and terminology of data science and data engineering
Build and use a technology stack that meets industry criteria
Master the methods for retrieving actionable business knowledge
Coordinate the handling of polyglot data types in a data lake for repeatable resultsWho This Book Is For
Data scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers