History textbook is a college-level, introductory textbook that covers the exciting subject of U.S. History. Volume II covers 1865 through the present day. Boundless works with subject matter experts to select the best open educational resources available on the web, review the content for quality, and create introductory, college-level textbooks designed to meet the study needs of university students.This textbook covers:Reconstruction: 1865-1877 -- The End of the War, The Aftermath of the War, The Battle over Reconstruction, Reconstruction in the South, The Reconstructed South, The Grant PresidencyThe Gilded Age: 1870-1900 -- The Gilded Age, The Second Industrial Revolution, The Rise of the City, The Rise of Big Business, The Rise of Immigration, Work in Industrial America, The Transformation of the West, Conquest in the West, The Transformation of the South, Politics in the Gilded Age, Urban Reform, Corruption and Reform, The Agrarian and Populist Movements, The Silver SolutionRace, Empire, and Culture in the Gilded Age: 1870-1900 -- Culture in the Gilded Age, Popular Culture, Cheap Amusements, Education, The Rise of Realism, Labor and Domestic Tensions, The Labor Wars, War, Empire, and an Emerging American World PowerThe Progressive Era: 1890-1917 -- The Progressive Era, Labor, Local, and Political Reform, The Politics of Progressivism, Grassroots Progressivism, Progressivism: Theory and Practice, Changing Ideas of Freedom, Roosevelt's Progressivism, Roosevelt's Second Term, From Roosevelt to Taft, Woodrow Wilson and Progressivism, The Limits of ProgressivismWorld War I: 1914-1919 -- The Wilson Administration, American Neutrality, America's Entry into the War, America and WWI, The War at Home, The "American", The Fight for Peace, Diplomacy & Negotiations at the War's End, The Transition to Peace: 1919-21From the New Era to the Great Depression: 1920-1933 -- The New Era, The Roaring Twenties, The Culture of Change, Resistance to Change, Wall Street Crash of 1929, The Great DepressionThe New Deal: 1933-1940 -- Franklin D. Roosevelt and the First New Deal, The New Deal, Critical Interpretations of the New Deal, The Social Cost of the Depression, Toward a Welfare State, Roosevelt's Second Term, Culture in the Thirties, The Second New Deal, The Legacy of the New DealFrom Isolation to World War II: 1930-1943 -- Non-Interventionism, The Beginning of the War, Conflict in Europe, Conflict in the Pacific, America's Early Involvement, Mobilization in the U.S., Social Effects of the War, The War in Germany, The War in the Pacific, The End of WWIIThe Cold War: 1947-1991 -- Origins of the Cold War, The Cold War, Truman and the Fair Deal, The Cold War and KoreaThe Politics and Culture of Abundance: 1943-1960 -- The Politics of Abundance, The Culture of Abundance, The Eisenhower Administration, The Policy of Containment, The Emergence of the Civil Rights MovementThe Sixties: 1960-1969 -- The Election of 1960, The Expansion of the Civil Rights Movement, Counterculture, The John F. Kennedy Administration, The Lyndon B. Johnson AdministrationThe Conservative Turn of America: 1968-1989 -- The Nixon Administration, Watergate, The Ford Administration, The Carter Administration, The Reagan AdministrationThe Challenges of Globalization and the Coming Century: After 1989 -- The George H.W. Bush Administration, America's Emerging Culture, The Clinton Administration, Globalization and the U.S.

    Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code f

    "Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, the author uses the flexible Python programming language to show how to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

    It runs on PCs rather than servers and is ideal for small- to mid-sized businesses and households. But Access is still intimidating to learn. It doesn't help that each new version crammed in yet another set of features; so many, in fact, that even the pros don't know where to find them all. Access 2007 breaks this pattern with some of the most dramatic changes users have seen since Office 95. Most obvious is the thoroughly redesigned user interface, with its tabbed toolbar (or "Ribbon") that makes features easy to locate and use. The features list also includes several long-awaited changes. One thing that hasn't improved is Microsoft's documentation. To learn the ins and outs of all the features in Access 2007, Microsoft merely offers online help.Access 2007: The Missing Manual was written from the ground up for this redesigned application. You will learn how to design complete databases, maintain them, search for valuable nuggets of information, and build attractive forms for quick-and-easy data entry. You'll even delve into the black art of Access programming (including macros and Visual Basic), and pick up valuable tricks and techniques to automate common tasks -- even if you've never touched a line of code before. You will also learn all about the new prebuilt databases you can customize to fit your needs, and how the new complex data feature will simplify your life. With plenty of downloadable examples, this objective and witty book will turn an Access neophyte into a true master.

    This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham’s package development philosophy. In the process, you’ll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language. Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your package writing over time. You’ll learn to focus on what you want your package to do, rather than think about package structure. Learn about the most useful components of an R package, including vignettes and unit tests Automate anything you can, taking advantage of the years of development experience embodied in devtools Get tips on good style, such as organizing functions into files Streamline your development process with devtools Learn the best way to submit your package to the Comprehensive R Archive Network (CRAN) Learn from a well-respected member of the R community who created 30 R packages, including ggplot2, dplyr, and tidyr

    In this updated edition author Bruce Hyslop uses crystal-clear instructions and friendly prose to introduce you to all of today's HTML and CSS essentials. The book has been refreshed to feature current web design best practices. You'll learn how to design, structure, and format your website. You'll learn about the new elements and form input types in HTML5. You'll create and use images, links, styles, and forms; and you'll add video, audio, and other multimedia to your site. You'll learn how to add visual effects with CSS3. You'll understand web standards and learn from code examples that reflect today's best practices. Finally, you will test and debug your site, and publish it to the web. Throughout the book, the author covers all of HTML and offers essential coverage of HTML5 and CSS techniques.

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    

    This unique case-based text places the reader squarely in the classroom while providing a framework that teaches readers to apply in-depth coverage of current and future computer, multimedia, Internet/intranet, distance learning, and audio/visual technologies to classroom instruction. Don't just read about technology integration - experience it! In addition to its' unique case-based approach the new edition now includes a new ASSURE Learning in Action DVD. This dvd, located in every copy of the text, provides current video of today's teachers using technology and media to improve learning for students across grade levels and content areas, rubric templates, a lesson plan builder, and skill-builder activites.

    It is also about Python. However, there is much more. The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum. In addition, a beginner needs to be given the opportunity to be successful and gain confidence. This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum. Even though the second course is considered more advanced than the first course, this book assumes you are beginners at this level. You may still be struggling with some of the basic ideas and skills from a first computer science course and yet be ready to further explore the discipline and continue to practice problem solving. We cover abstract data types and data structures, writing algorithms, and solving problems. We look at a number of data structures and solve classic problems that arise. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.

    The Windows Command Line Beginner's Guide gives users new to the Windows command line an overview of the Command Prompt, from simple tasks to network configuration.In the Guide, you'll learn how to:-Manage the Command Prompt.-Copy & paste from the Windows Command Prompt.-Create batch files.-Remotely manage Windows machines from the command line.-Manage disks, partitions, and volumes.-Set an IP address and configure other network settings.-Set and manage NTFS and file sharing permissions.-Customize and modify the Command Prompt.-Create and manage file shares.-Copy, move, and delete files and directories from the command line.-Manage PDF files and office documents from the command line.-And many other topics.

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    Students will find concepts easy-to-understand through the clear writing style and full-color figures that illustrate current technology and trends. Examples and cases are drawn from actual systems projects that enable students to learn in the context of solving problems, much like the ones they will encounter on the job. This approach, combined with motivating tools such as the SCR Associates interactive Web-Based Case Study, Systems Analyst's Toolkit, the Student Study Tool on CD-ROM, and more, makes Systems Analysis and Design, Seventh Edition a wise and exciting choice for your introductory systems analysis and design class.

    It covers the basics of computer programming, including variables and values, functions, conditionals and control flow, program development and debugging. Later chapters cover basic algorithms and data structures.