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Preview: Peachpit: Safari Books Online

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Develop an Advanced Cross-platform App Using Xamarin.Forms and XAML

2017/12/14December 8, 2017

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Create stunning cross-platform applications with the iOS and Android mobile platforms About This Video Gain a thorough understanding of the MVVM Architecture pattern Implement a Navigation Service Interface and class to navigate between models Write the shared business logic for your application Work with (and implement the classes contained within) the SQLite class Implement data-binding within a Xamarin.Forms XAML document In Detail Xamarin.Forms. is one of the most powerful cross-platform mobile development frameworks for creating stunning cross-platform applications with the iOS and Android mobile platforms. XAML (eXtensible Application Markup Language) allows developers to define user interfaces in Xamarin.Forms applications using markup rather than code. This video course begins by showing you how to write a Medicine Tracking app using Xamarin.Forms and SQLite for data handling. This application will allow you to track your daily medications. You will also walk through the MVVM architectural pattern and the Xamarin.Forms Navigation API, before creating the Navigation Service Interface and class that will be used to navigate between your view models. You will then set up the solution for your project, write the shared business logic used across the application, and write an introduction to SQLite. Towards the end of the course, you will master the Xamarin.Forms platform architecture, and then write the user interface for your application, bind it to the UI, and delve more deeply into XAML and how data-binding works in Xamarin.Forms




Getting Started with PowerShell DSC

2017/12/14December 11, 2017

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Start to explore, implement, and manage the diverse IT platform (Cloud/ Linux/ Windows/ Containers) with PowerShell DSC About This Video Learn to manage complete Infrastructure as a Code or Configuration by authoring custom DSC solutions for your business Understand DevOps with PowerShell DSC deployment patterns using real-world examples. Take the step-by-step journey of implementing and managing the diverse IT platform (Cloud/Linux/Windows/Containers) with PowerShell DSC In Detail Windows PowerShell is a scripting language especially designed for system administration and it lets you manage computers from the command line. PowerShell DSC enables you to deploy and manage configuration data for software services and also manages the environment in which these services run. The main goal of this video is to teach you how to configure, deploy, and manage your system using the new features of PowerShell v6 DSC with Windows 10 and Windows Server 2016. This video begins with the basic fundamentals of PowerShell DSC, covering the architecture and components of the Desired Sate Configuration. Next, this video familiarizes you with sets of PowerShell language extensions and new PowerShell commands. Later, this course helps you understand and create DSC configurations with the help of practical examples, and also teaches you to create DSC custom resources for your custom applications. Finally, you will learn to deploy a real-world application using PowerShell DSC. By the end of the video, you will be more familiar with the powerful Desired State Configuration platform, which helps you achieve continuous delivery and efficient management and easy deployment of data for systems. Style and Approach This video is a quick-start guide to the fundamentals of Powershell DSC.




Training and Exporting Machine Learning Models in Spark

2017/12/14December 12, 2017

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Spark ML provides a rich set of tools and models for training, scoring, evaluating, and exporting machine learning models. This video walks you through each step in the process. You’ll explore the basics of Spark’s DataFrames, Transformer, Estimator, Pipeline, and Parameter, and how to utilize the Spark API to create model uniformity and comparability. You'll learn how to create meaningful models and labels from a raw dataset; train and score a variety of models; target price predictions; compare results using MAE, MSE, and other scores; and employ the SparkML evaluator to automate the parameter-tuning process using cross validation. To complete the lesson, you'll learn to export and serialize a Spark trained model as PMML (an industry standard for model serialization), so you can deploy in applications outside the Spark cluster environment. Gain hands-on experience in training, scoring, evaluating, and exporting machine learning models Understand how to utilize the Spark API to create model uniformity and comparability Explore feature extraction, training, scoring, and hyper-parameter tuning using Spark ML Understand how to use a model trained in Spark and deploy it in other applications Hollin Wilkins is the cofounder of Combust, Inc., an ML/AI start-up in the SF Bay Area. A data scientist and software engineer formerly with True Car, Hollin has worked with machine learning, high-performance microservices, and software development since 2011. Jason Slepicka is a senior data engineer with DataScience, where he builds pipelines and data science platform infrastructure. Jason is working on his PhD in Computer Science at the University of Southern California Information Sciences Institute.




An Introduction to Machine Learning Models in Production

2017/12/14December 12, 2017

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This course lays out the common architecture, infrastructure, and theoretical considerations for managing an enterprise machine learning (ML) model pipeline. Because automation is the key to effective operations, you'll learn about open source tools like Spark, Hive, ModelDB, and Docker and how they're used to bridge the gap between individual models and a reproducible pipeline. You'll also learn how effective data teams operate; why they use a common process for building, training, deploying, and maintaining ML models; and how they're able to seamlessly push models into production. The course is designed for the data engineer transitioning to the cloud and for the data scientist ready to use model deployment pipelines that are reproducible and automated. Learners should have basic familiarity with: cloud platforms like Amazon Web Services; Scala or Python; Hadoop, Spark, or Pandas; SBT or Maven; Bash, Docker, and REST. Understand how to set-up and manage an enterprise ML model pipeline Learn the common components that make up enterprise ML model pipelines Explore the use and purpose of pipeline tools like Spark, Hive, ModelDB, and Docker Discover the gaps in the Spark ecosystem for maintaining and deploying ML pipelines Learn how to move from creating one-off models to building a reproducible automated pipeline Jason Slepicka is a senior data engineer with Los Angeles based DataScience, where he builds pipelines and data science platform infrastructure. He has a decade of experience integrating data to support efforts like fighting human trafficking for DARPA, exploring the evolution of evolvability in yeast, and tracking intruders in computer networks. Jason has both a Bachelor's and Master’s in Computer Science from the University of Arizona and is working on his PhD in Computer Science at the University of Southern California Information Sciences Institute.




Nutrition for Dancers

2017/12/14December 12, 2017

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Dancers are top performance athletes on stage – to keep fit and healthy proper nutrition is an integral part of an optimal dance training. Nutrition for Dancers provides the principles of nutrition for dancers of all genres. Authors Liane Simmel and Eva- Maria Kraft clarify widespread nutritional mistakes and give advice on how a healthy diet can be incorporated into the everyday life of dancers.




Virtual Reality Filmmaking

2017/12/14November 22, 2017

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Virtual Reality Filmmaking presents a comprehensive guide to the use of virtual reality in filmmaking, including narrative, documentary, live event production, and more. Written by Celine Tricart, a filmmaker and an expert in new technologies, the book provides a hands-on guide to creative filmmaking in this exciting new medium, and includes coverage on how to make a film in VR from start to finish. Topics covered include: The history of VR; VR cameras; Game engines and interactive VR; The foundations of VR storytelling; Techniques for shooting in live action VR; VR postproduction and visual effects; VR distribution; Interviews with experts in the field including the Emmy-winning studios Felix & Paul and Oculus Story Studio, Wevr, Viacom, Fox Sports, Sundance’s New Frontier, and more.




Developing Node Applications on IBM Cloud

2017/12/14December 12, 2017

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Abstract This IBM® Redbooks® publication explains how to create various applications based on Node, and deploy and run them on IBM Cloud. This book includes the following exercises: Develop a Hello World application in Node on IBM Cloud. Use asynchronous callback to call an external service. Create an Express application. Build a rich front-end application by using React and ES6. During these exercises, you will perform these tasks: Create an IBM SDK for Node application. Write your first Node application. Deploy an IBM SDK for Node application on an IBM Cloud account. Create a Node module and use it in your code. Understand asynchronous callbacks and know how to use it to call an external service. Understand IBM Watson™ Language Translator service. Create a Hello World Express application. Create a simple HTML view for your application. Understand Express routing. Use third-party modules in Node. Understand IBM Watson® Natural Language Understanding service. Use a Git repository on IBM Cloud DevOps services. Understand Delivery Pipeline. Understand how to clone an IBM Cloud application. Use React to create interactive web pages. Understand the following concepts of ES6: Classes, arrow functions, and promises. This book is for beginner and experienced developers who want to start coding Node applications on IBM Cloud.




Basic Statistics and Data Mining for Data Science

2017/12/14December 8, 2017

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Enter the world of Statistics, Data Analysis and Data Science! About This Video This comprehensive video tutorial will ensure that you build on your knowledge of statistics and learn how to apply it in the field of data science You’ll learn when to use different statistical techniques, how to set up different analyses, and how to interpret the results This video course follows a step-by-step approach to ensure that you get the basics right In Detail Data science is an ever-evolving field, with exponentially growing popularity. Data science includes techniques and theories extracted from the fields of statistics, computer science, and most importantly machine learning, databases, and visualization. This video course consists of step-by-step introductions to analyze data and the basics of statistics. The first chapter focuses on the steps to analyze data and which summary statistics are relevant given the type of data you are summarizing. The second chapter continues by focusing on summarizing individual variables and specifically some of the reasons users need to summarize variables. This chapter also illustrates several procedures, such as how to run and interpret frequencies and how to create various graphs. The third chapter introduces the idea of inferential statistics, probability, and hypothesis testing. The rest of the chapters show you how to perform and interpret the results of basic statistical analyses (chi-square, independent and paired sample t-tests, one-way ANOVA, post-hoc tests, and bivariate correlations) and graphical displays (clustered bar charts, error bar charts, and scatterplots). You will also learn when to use different statistical techniques, how to set up different analyses, and how to interpret the results.




Ensemble Learning

2017/12/14January 1, 2018

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This video explains ensemble learning along with its use cases and variations. There are six clips in this video: Ensemble Learning Overview . This clip explains ensemble learning and the three main reasons why this paradigm is so important. Ensemble learning is the best way to improve the performance of a single machine learning algorithm. Learn the necessary prerequisites of base learners for ensemble learning. Drawbacks of Single Learner . This clip explores the limitations of a single classifier and how these limitations can be tackled by ensemble learning. See how statistical learning and the representational problem could be tackled by ensemble learning. Types of Ensemble Methods . This clip provides an overview to both Homogeneous Ensemble Learning and Heterogeneous Ensemble Learning. Heterogeneous Ensemble Methods . This clip covers the two types of heterogeneous ensemble learning which are Stacking and Cascade Generalization. Homogeneous Ensemble Methods . This clip covers the two types of homogeneous ensemble learning methods which are bagging and boosting. Adaboost (Adaptive Boosting) . This clip covers Adaboost in detail including the Adaboost Algorithm, Theoretical Guarantees on training error, the expected and observed behavior of Adaboost, and its advantages and disadvantages.




Computer Science Illuminated, 6th Edition

2017/12/14January 27, 2015

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Each new print copy includes Navigate 2 Advantage Access that unlocks a comprehensive and interactive eBook, student practice activities and assessments, a full suite of instructor resources, and learning analytics reporting tools. Fully revised and updated, the Sixth Edition of the best-selling text Computer Science Illuminated retains the accessibility and in-depth coverage of previous editions, while incorporating all-new material on cutting-edge issues in computer science. Authored by the award-winning Nell Dale and John Lewis, Computer Science Illuminated’s unique and innovative layered approach moves through the levels of computing from an organized, language-neutral perspective. Designed for the introductory computing and computer science course, this student-friendly Sixth Edition provides students with a solid foundation for further study, and offers non-majors a complete introduction to computing. Key Features of the Sixth Edition include: Access to Navigate 2 online learning materials including a comprehensive and interactive eBook, student practice activities and assessments, learning analytics reporting tools, and more Completely revised sections on HTML and CSS Updates regarding Top Level Domains, Social Networks, and Google Analytics All-new section on Internet management, including ICANN control and net neutrality New design, including fully revised figures and tables New and updated Did You Know callouts are included in the chapter margins New and revised Ethical Issues and Biographies throughout emphasize the history and breadth of computing Available in our customizable PUBLISH platform A collection of programming language chapters are available as low-cost bundling options. Available chapters include: Java, C++, Python, Alice, SQL, VB.NET, RUBY, Perl, Pascal, and JavaScript. With Navigate 2, technology and content combine to expand the reach of your classroom. Whether you teach an online, hybrid, or traditional classroom-based course, Navigate 2 delivers unbeatable value. Experience Navigate 2 today at www.jblnavigate.com/2