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.

    If you're new to Elixir, its functional style can seem difficult, but with help from this hands-on introduction, you'll scale the learning curve and discover how enjoyable, powerful, and fun this language can be. Elixir combines the robust functional programming of Erlang with an approach that looks more like Ruby and reaches toward metaprogramming with powerful macro features.Authors Simon St. Laurent and J. David Eisenberg show you how to write simple Elixir programs by teaching you one skill at a time. You’ll learn about pattern matching, recursion, message passing, process-oriented programming, and establishing pathways for data rather than telling it where to go. By the end of your journey, you’ll understand why Elixir is ideal for concurrency and resilience.* Get comfortable with IEx, Elixir's command line interface* Become familiar with Elixir’s basic structures by working with numbers* Discover atoms, pattern matching, and guards: the foundations of your program structure* Delve into the heart of Elixir processing with recursion, strings, lists, and higher-order functions* Create processes, send messages among them, and apply pattern matching to incoming messages* Store and manipulate structured data with Erlang Term * Storage (ETS) and the Mnesia database* Build resilient applications with the Open Telecom Platform (OTP)* Define macros with Elixir's meta-programming tools.

    This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

    A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow.

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    

    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

    But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options.This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization.Explore the basic concepts of color as a tool to highlight, distinguish, or represent a valueUnderstand the importance of redundant coding to ensure you provide key information in multiple waysUse the book's visualizations directory, a graphical guide to commonly used types of data visualizationsGet extensive examples of good and bad figuresLearn how to use figures in a document or report and how employ them effectively to tell a compelling story

    In an instructive and conversational tone, Cody clearly explains how to program SAS, illustrating with one or more real-life examples and giving a detailed description of how the program works.

    It has become popular in recent years because of its simplicity, conciseness, and clarity. This book teaches functional programming as a way of thinking and problem solving, using Haskell, the most popular purely functional language. Rather than using the conventional (boring) mathematical examples commonly found in other programming language textbooks, the author uses examples drawn from multimedia applications, including graphics, animation, and computer music, thus rewarding the reader with working programs for inherently more interesting applications. Aimed at both beginning and advanced programmers, this tutorial begins with a gentle introduction to functional programming and moves rapidly on to more advanced topics. Details about progamming in Haskell are presented in boxes throughout the text so they can be easily found and referred to.

    

    'Programming The World Wide Web', written by bestselling author, Robert Sebesta, provides a comprehensive introduction to the programming tools and skills required for building and maintaining server sites on the Web.

     Offers extensively updated coverage, new problem sets, and chapter-ending material to enhance the book’s relevance to today’s engineers and scientists. Includes new problem sets demonstrating updated applications to engineering as well as biological, physical, and computer science. Emphasizes key ideas as well as the risks and hazards associated with practical application of the material. Includes new material on topics including: difference between discrete and continuous measurements; binary data; quartiles; importance of experimental design; “dummy” variables; rules for expectations and variances of linear functions; Poisson distribution; Weibull and lognormal distributions; central limit theorem, and data plotting. Introduces Bayesian statistics, including its applications to many fields. For those interested in learning more about probability and statistics.

    Cambridge IGCSE Accounting has been written as per the specifications of the Cambridge IGCSE Accounting Syllabus. Accounting principles and practices have been explained in simple language and lucid style to enhance the accessibility of the contents to students whose first language is not English.

    Loosely based on neuron behavior inside of human brains, these systems are rapidly catching up with the intelligence of their human creators, defeating the world champion Go player, achieving superhuman performance on video games, driving cars, translating languages, and sometimes even helping law enforcement fight crime. Deep Learning is a revolution that is changing every industry across the globe.Grokking Deep Learning is the perfect place to begin your deep learning journey. Rather than just learn the “black box” API of some library or framework, you will actually understand how to build these algorithms completely from scratch. You will understand how Deep Learning is able to learn at levels greater than humans. You will be able to understand the “brain” behind state-of-the-art Artificial Intelligence. Furthermore, unlike other courses that assume advanced knowledge of Calculus and leverage complex mathematical notation, if you’re a Python hacker who passed high-school algebra, you’re ready to go. And at the end, you’ll even build an A.I. that will learn to defeat you in a classic Atari game.