Learning Android, Gargenta Marko

Автор: Sillars Doug
Название: High Performance Android Apps: Improve Ratings with Speed, Optimizations, and Testing
ISBN: 1491912510 ISBN-13(EAN): 9781491912515
Издательство: Wiley
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Цена: 5701.00 р.
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Описание: Unique and clever ideas are important when building a hot-selling Android app, but the real drivers for success are speed, efficiency, and power management. With this practical guide, you`ll learn the major performance issues confronting Android app developers, and the tools you need to diagnose problems early. .

Автор: Chad Udell
Название: Mastering Mobile Learning
ISBN: 1118884914 ISBN-13(EAN): 9781118884911
Издательство: Wiley
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Цена: 6732.00 р.
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Описание: Discover the strategies, tools, and technologies necessary for developing successful mobile learning programs In the modern, rapidly-expanding mobile learning environment, only clear guidelines and state-of-the-art technologies will stand up to the challenges that lie ahead.

Автор: Shrivastava Mohith
Название: Learning Salesforce Lightning Application Development
ISBN: 1787124673 ISBN-13(EAN): 9781787124677
Издательство: Неизвестно
Цена: 7171.00 р.
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Описание: Built on the Salesforce App Cloud, the new Salesforce Lightning Experience combines three major components: Lightning Design System, Lightning App Builder, and Lightning Components, to provide an enhanced user experience. This book will enable you to quickly create modern, enterprise apps with Lightning.

Автор: Rafols, Raimon Dawson, Laurence
Название: Learning android application development
ISBN: 1785286110 ISBN-13(EAN): 9781785286117
Издательство: Неизвестно
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Цена: 8091.00 р.
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Автор: Brothaler Kevin
Название: OpenGL ES 2 for Android
ISBN: 1937785343 ISBN-13(EAN): 9781937785345
Издательство: Wiley
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Цена: 4831.00 р.
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Описание: It`s never been a better time to learn how to create your own 3D games and live wallpaper for Android. You`ll find out all about shaders and the OpenGL pipeline, and discover the power of OpenGL ES 2.0, which is much more feature-rich than its predecessor.

Автор: Allan Alasdair
Название: Learning IOS Programming
ISBN: 1449359345 ISBN-13(EAN): 9781449359348
Издательство: Wiley
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Цена: 4434.00 р.
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Описание: Get a rapid introduction to iPhone, iPad, and iPod touch programming. With this easy-to-follow guide, you`ll learn how to develop your first marketable iOS application, from opening Xcode to submitting your product to the App Store

Автор: Eisenman Bonnie
Название: Learning React Native: Building Native Mobile Apps with JavaScript
ISBN: 1491989149 ISBN-13(EAN): 9781491989142
Издательство: Wiley
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Цена: 7602.00 р.
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Описание: Get a practical introduction to React Native, the JavaScript framework for writing and deploying fully featured mobile apps that render natively. The second edition of this hands-on guide shows you how to build applications that target iOS, Android, and other mobile platforms instead of browsers.

Автор: Buttfield-Addison Paris, Manning Jon, Nugent Tim
Название: Learning Swift: Building Apps for Macos, IOS, and Beyond
ISBN: 149198757X ISBN-13(EAN): 9781491987575
Издательство: Wiley
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Цена: 6334.00 р.
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Описание: Dive into Swift 4.x, the latest version of Apple`s easy-to-use programming language for macOS, iOS, watchOS, and tvOS. With this practical guide, you`ll quickly get up to speed on language basics and then use Swift to build three complete apps.

Автор: Tiano, Jak
Название: Learning xcode
ISBN: 1785885723 ISBN-13(EAN): 9781785885723
Издательство: Неизвестно
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Цена: 10114.00 р.
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Автор: Peters Jonas, Janzing Dominik, Scholkopf Bernhard
Название: Elements of Causal Inference: Foundations and Learning Algorithms
ISBN: 0262037319 ISBN-13(EAN): 9780262037310
Издательство: MIT Press
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Цена: 7719.00 р.
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Описание:

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.

The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data.

After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem.

The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.