This collection is directed towards anyone interested in the use of mobile learning for various applications. Readers will discover how to design learning materials for delivery on mobile technology and become familiar with the best practices of other educators, trainers, and researchers in the field as well as the most recent research initiatives in mobile learning. Businesses and governments can find out how to deliver timely information to staff using mobile devices. Professors and trainers can use this book as a textbook in courses on distance education, mobile learning, and educational technology. In fact, the book can be used by anyone interested in delivering education and training at a distance, but especially by graduate students of emerging technology in learning.
Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming language Key FeaturesUse data analytics and machine learning in a sales and marketing contextGain insights from data to make better business decisionsBuild your experience and confidence with realistic hands-on practiceBook Description Unleash the power of data to reach your marketing goals with this practical guide to data science for business. This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects. You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions. As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior. By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making. What you will learnLoad, clean, and explore sales and marketing data using pandasForm and test hypotheses using real data sets and analytics toolsVisualize patterns in customer behavior using MatplotlibUse advanced machine learning models like random forest and SVMUse various unsupervised learning algorithms for customer segmentationUse supervised learning techniques for sales predictionEvaluate and compare different models to get the best outcomesOptimize models with hyperparameter tuning and SMOTEWho this book is for This marketing book is for anyone who wants to learn how to use Python for cutting-edge marketing analytics. Whether you're a developer who wants to move into marketing, or a marketing analyst who wants to learn more sophisticated tools and techniques, this book will get you on the right path. Basic prior knowledge of Python and experience working with data will help you access this book more easily.
This book constitutes the refereed proceedings of the 7th European Conference on Technology Enhanced Learning, EC-TEL 2012, held in Saarbrücken, Germany, in September 2012. The 26 revised full papers presented were carefully reviewed and selected from 130 submissions. The book also includes 12 short papers, 16 demonstration papers, 11 poster papers, and 1 invited paper. Specifically, the programme and organizing structure was formed through the themes: mobile learning and context; serious and educational games; collaborative learning; organisational and workplace learning; learning analytics and retrieval; personalised and adaptive learning; learning environments; academic learning and context; and, learning facilitation by semantic means.
This updated edition describes both the mathematical theory behind a modern photorealistic rendering system as well as its practical implementation. Through the ideas and software in this book, designers will learn to design and employ a full-featured rendering system for creating stunning imagery. Includes a companion site complete with source code for the rendering system described in the book, with support for Windows, OS X, and Linux.
An updated, concise reference for the Java programming language, version 8.0, and essential parts of its class languages, offering more detail than a standard textbook. The third edition of Java Precisely provides a concise description of the Java programming language, version 8.0. It offers a quick reference for the reader who has already learned (or is learning) Java from a standard textbook and who wants to know the language in more detail. The book presents the entire Java programming language and essential parts of the class libraries: the collection classes, the input-output classes, the stream libraries and Java 8's facilities for parallel programming, and the functional interfaces used for that. Though written informally, the book describes the language in detail and offers many examples. For clarity, most of the general rules appear on left-hand pages with the relevant examples directly opposite on the right-hand pages. All examples are fragments of legal Java programs. The complete ready-to-run example programs are available on the book's website. This third edition adds material about functional parallel processing of arrays; default and static methods on interfaces; a brief description of the memory model and visibility across concurrent threads; lambda expressions, method reference expressions, and the related functional interfaces; and stream processing, including parallel programming and collectors.
This book constitutes the refereed proceedings of the International Conference on Informatics in Secondary Schools - Evolution and Perspectives, ISSEP 2006, held in Vilnius, Lithuania in November 2006. The 29 revised full papers presented were carefully reviewed and selected from 204 submissions. A broad variety of topics related to teaching informatics in secondary schools is addressed.
Exploring recent developments in the rapidly evolving field of game real-time rendering, GPU Zen assembles a high-quality collection of cutting-edge contributions for programming the GPU. Rendering (Patrick Cozzi)1. Adaptive GPU Tessellation with Compute Shaders by Jad Khoury, Jonathan Dupuy, and Christophe Riccio2. Applying Vectorized Visibility on All frequency Direct Illumination by Ho Chun Leung, Tze Yui Ho, Zhenni Wang, Chi Sing Leung, Eric Wing Ming Wong3. Non-periodic Tiling of Noise-based Procedural Textures by Aleksandr Kirillov4. Rendering Surgery Simulation with Vulkan by Nicholas Milef, Di Qi, and Suvranu De5. Skinned Decals by Hawar DoghramachiEnvironmental Effects (Wolfgang Engel)1. Real-Time Fluid Simulation in Shadow of the Tomb Raider by Peter Sikachev, Martin Palko and Alexandre Chekroun2. Real-time Snow Deformation in Horizon Zero Dawn: The Frozen Wilds by Kevin ÖrtegrenShadows (Maurizio Vives)1. Soft Shadow Approximation for Dappled Light Sources by Mariano Merchante2. Parallax-Corrected Cached Shadow Maps by Pavlo Turchyn3D Engine Design (Wessam Bahnassi)1. Real-Time Layered Materials Compositing Using Spatial Clustering Encoding by Sergey Makeev2. Procedural Stochastic Textures by Tiling and Blending by Thomas Deliot and Eric Heitz3. A Ray Casting Technique for Baked Texture Generation by Alain Galvan and Jeff Russell4. Writing an efficient Vulkan renderer by Arseny Kapoulkine5. glTF - Runtime 3D Asset Delivery by Marco HutterRay Tracing (Anton Kaplanyan)1. Real-Time Ray-Traced One-Bounce Caustics by Holger Gruen2. Adaptive Anti-Aliasing using Conservative Rasterization and GPU Ray Tracing by Rahul Sathe, Holger Gruen, Adam Marrs, Josef Spjut, Morgan McGuire, Yury Uralsky
Updated for OpenCV 4 and Python 3, this book covers the latest on depth cameras, 3D tracking, augmented reality, and deep neural networks, helping you solve real-world computer vision problems with practical code Key Features Build powerful computer vision applications in concise code with OpenCV 4 and Python 3 Learn the fundamental concepts of image processing, object classification, and 2D and 3D tracking Train, use, and understand machine learning models such as Support Vector Machines (SVMs) and neural networks Book Description Computer vision is a rapidly evolving science, encompassing diverse applications and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You'll be able to put theory into practice by building apps with OpenCV 4 and Python 3. You'll start by understanding OpenCV 4 and how to set it up with Python 3 on various platforms. Next, you'll learn how to perform basic operations such as reading, writing, manipulating, and displaying still images, videos, and camera feeds. From taking you through image processing, video analysis, and depth estimation and segmentation, to helping you gain practice by building a GUI app, this book ensures you'll have opportunities for hands-on activities. Next, you'll tackle two popular challenges: face detection and face recognition. You'll also learn about object classification and machine learning concepts, which will enable you to create and use object detectors and classifiers, and even track objects in movies or video camera feed. Later, you'll develop your skills in 3D tracking and augmented reality. Finally, you'll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age. By the end of this book, you'll have the skills you need to execute real-world computer vision projects. What you will learn Install and familiarize yourself with OpenCV 4's Python 3 bindings Understand image processing and video analysis basics Use a depth camera to distinguish foreground and background regions Detect and identify objects, and track their motion in videos Train and use your own models to match images and classify objects Detect and recognize faces, and classify their gender and age Build an augmented reality application to track an image in 3D Work with machine learning models, including SVMs, artificial neural networks (ANNs), and deep neural networks (DNNs) Who this book is for If you are interested in learning computer vision, machine learning, and OpenCV in the context of practical real-world applications, then this book is for you. This OpenCV book will also be useful for anyone getting started with computer vision as well as experts who want to stay up-to-date with OpenCV 4 and Python 3. Although no prior knowledge of image processing, computer vision or machine learning is required, familiarity with basic Python programming is a must.
"An under-the-hood look at how the Ruby programming language runs code. Extensively illustrated with complete explanations and hands-on experiments. Covers Ruby 2.x"--
This timely introduction to the emerging field of mobile learning uses case studies written by experts in the field to explain the technologies involved, their applications and the multiple effects on pedagogical and social practice.
