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Dive into the RStudio Integrated Development Environment (IDE) for using and programming R, the popular open source software for statistical computing and graphics. This concise book provides new and experienced users with an overview of RStudio, as well as hands-on instructions for analyzing data, generating reports, and developing R software packages. The open source RStudio IDE brings many powerful coding tools together into an intuitive, easy-to-learn interface. With this guide, you’ll learn how to use its main components—including the console, source code editor, and data viewer—through descriptions and case studies. Getting Started with RStudio serves as both a reference and introduction to this unique IDE. Use RStudio to provide enhanced support for interactive R sessions Clean and format raw data quickly with several RStudio components Edit R commands with RStudio’s code editor, and combine them into functions Easily locate and use more than 3,000 add-on packages in R’s CRAN service Develop and document your own R packages with the code editor and related components Create one-click PDF reports in RStudio with a mix of text and R output
Know how to use the features of Visual Studio 2019 and utilize the IDE correctly to become your one-stop solution for creating quality code. Learn what's new in VS 2019 and explore the existing features of Visual Studio so you can use them more efficiently. Getting Started with Visual Studio 2019 begins with an overview of Visual Studio and explores new features such as Visual Studio Live Share, Visual Studio Search, Solution Filters, and Intellicode. Author Dirk Strauss teaches you how to create project templates, write code snippets, and manage NuGet packages. You will learn how to: debug your code using breakpoints and step into specific methods, use data tips, and utilize the DebuggerDisplay attribute. You will then move on to learn unit testing and explore the tools provided by Visual Studio to create and run unit tests. The book also covers source control integration in Visual Studio and how to use GitHub to implement a source control strategy in your code. What You Will LearnCreate and use code snippets in Visual Studio 2019Utilize diagnostic tools and the Immediate window for code debuggingGenerate unit tests with IntelliTestUse NuGet in applicationsCreate and handle pull requests Who This Book Is For Beginners and software developers working on the .NET stack
Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning (DL) and Machine Learning (ML) concepts. DL and ML are the most sought-after domains, requiring a deep understanding – and this book gives no less than that. This book enables the reader to build innovative and useful applications based on ML and DL. Starting with the basics of neural networks, and continuing through the architecture of various types of CNNs, RNNs, LSTM, and more till the end of the book, each and every topic is given the utmost care and shaped professionally and comprehensively. Key Features Includes the smooth transition from ML concepts to DL concepts Line-by-line explanations have been provided for all the coding-based examples Includes a lot of real-time examples and interview questions that will prepare the reader to take up a job in ML/DL right away Even a person with a non-computer-science background can benefit from this book by following the theory, examples, case studies, and code snippets Every chapter starts with the objective and ends with a set of quiz questions to test the reader’s understanding Includes references to the related YouTube videos that provide additional guidance AI is a domain for everyone. This book is targeted toward everyone irrespective of their field of specialization. Graduates and researchers in deep learning will find this book useful.
Do you have a basic knowledge of C# language? Would you like to be able to expand on this and take it to the next level? This is the book that will take you there! There are so many computer programming languages currently available that it can seem like a bewildering choice. C# is just one of them, of course, but if you already have a passing knowledge of its functions then expanding on that would seem like the obvious way forward. In this book, C#: The Ultimate Intermediate Guide To Learn C# Programming Step By Step, you can build on your previous experiences with C# and increase your knowledge with it, through chapters on: • How C# was conceived and where it came from • C# interfaces and how to use them • Advanced decision statements and flow control • The different functions that are available • An introduction to garbage collections • Asynchronous programming and what it does • And much more… Perfect for intermediates, written in a crisp format and in a language that is easily understood, C#: The Ultimate Intermediate Guide To Learn C# Programming Step By Step will help you take the next step up from the basics of C# quickly and seamlessly. Get yourself a copy today and take the next step in your programming adventure now!
Praise for previous editions: "Gandrud has written a great outline of how a fully reproducible research project should look from start to finish, with brief explanations of each tool that he uses along the way... Advanced undergraduate students in mathematics, statistics, and similar fields as well as students just beginning their graduate studies would benefit the most from reading this book. Many more experienced R users or second-year graduate students might find themselves thinking, ‘I wish I’d read this book at the start of my studies, when I was first learning R!’...This book could be used as the main text for a class on reproducible research ..." (The American Statistician) Reproducible Research with R and R Studio, Third Edition brings together the skills and tools needed for doing and presenting computational research. Using straightforward examples, the book takes you through an entire reproducible research workflow. This practical workflow enables you to gather and analyze data as well as dynamically present results in print and on the web. Supplementary materials and example are available on the author’s website. New to the Third Edition Updated package recommendations, examples, URLs, and removed technologies no longer in regular use. More advanced R Markdown (and less LaTeX) in discussions of markup languages and examples. Stronger focus on reproducible working directory tools. Updated discussion of cloud storage services and persistent reproducible material citation. Added discussion of Jupyter notebooks and reproducible practices in industry. Examples of data manipulation with Tidyverse tibbles (in addition to standard data frames) and pivot_longer() and pivot_wider() functions for pivoting data. Features Incorporates the most important advances that have been developed since the editions were published Describes a complete reproducible research workflow, from data gathering to the presentation of results Shows how to automatically generate tables and figures using R Includes instructions on formatting a presentation document via markup languages Discusses cloud storage and versioning services, particularly Github Explains how to use Unix-like shell programs for working with large research projects
Jython is an open source implementation of the high-level, dynamic, object-oriented scripting language Python seamlessly integrated with the Java platform. The predecessor to Jython, JPython, is certified as 100% Pure Java. Jython is freely available for both commercial and noncommercial use and is distributed with source code. Jython is complementary to Java. The Definitive Guide to Jython, written by the official Jython team leads, covers Jython 2.5 (or 2.5.x)—from the basics to more advanced features. This book begins with a brief introduction to the language and then journeys through Jython’s different features and uses. The Definitive Guide to Jython is organized for beginners as well as advanced users of the language. The book provides a general overview of the Jython language itself, but it also includes intermediate and advanced topics regarding database, web, and graphical user interface (GUI) applications; Web services/SOA; and integration, concurrency, and parallelism, to name a few.
Written in a comprehensive yet accessible style, this Handbook introduces readers to a range of modern empirical methods with applications in microeconomics, illustrating how to use two of the most popular software packages, Stata and R, in microeconometric applications.
Looking for a silver bullet to accelerate EL achievement? There is none. But this, we promise: when EL specialists and general ed teachers pool their expertise, your ELs’ language development and content mastery will improve exponentially. Just ask the tens of thousands of Collaboration and Co-Teaching users and now, a new generation of educators, thanks to this all-new second edition: Collaborating for English Learners. Why this new edition? Because more than a decade of implementation has generated for Andrea Honigsfeld and Maria Dove new insight into what exemplary teacher collaboration looks like, which essential frameworks must be established, and how integrated approaches to ELD services benefit all stakeholders. Essentially a roadmap to the many different ways we can all work together, this second edition of Collaborating for English Learners features: All-new examples, case studies, illustrative video, and policy updates In-depth coverage of the full range of strategies and configurations for determining the best model to adopt Templates, planning guides, and other practical tools to put collaboration into practice Guidelines, self-assessments, and questionnaires for evaluating the strategies’ effectiveness By this time, the big benefits of teacher collaboration are well documented. Where teachers and schools struggle still is determining the best way to do so, especially when working with our ELs. That’s where Andrea Honigsfeld, Maria Dove, and their second edition of Collaborating for English Learners will prove absolutely indispensable. After all, there are no two better authorities.
Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition. The R language provides everything you need to do statistical work, but its structure can be difficult to master. These task-oriented recipes make you productive with R immediately. Solutions range from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem and includes a discussion that explains the solution and provides insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an intermediate user, this book will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform basic functions Simplify data input and output Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (ANOVA) Explore advanced statistical techniques, such as finding clusters in your data