Intuitive Biostatistics covers all the topics typically found in an introductory statistics text, but with the emphasis on confidence intervals rather than P values, making it easier for students to understand both. Additionally, it introduces a broad range of topics left out of most other introductory texts but used frequently in biomedical publications, including survival curves. multiple comparisons, sensitivity and specificity of lab tests, Bayesian thinking, lod scores, and logistic, proportional hazards and nonlinear regression. By emphasizing interpretation rather than calculation, this text provides a clear and virtually painless introduction to statistical principles for those students who will need to use statistics constantly in their work. In addition, its practical approach enables readers to understand the statistical results published in biological and medical journals.

    These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

    Aaron Judge (25 years old), Gary Sanchez (24), Luis Severino (23), and Greg Bird (24) could be even more talented than that 1990s’ “Core Four” group, according to manager Joe Girardi. And they’re not alone . . . The Yankees also have youthful players such as Aaron Hicks, Clint Frazier, Didi Gregorius, Tyler Austin, Miguel Andujar, Chance Adams, Jordan Montgomery and Tyler Wade making their names known.Beginning with Judge and Sanchez competing at the 2017 Home Run Derby, when Judge―the 6-foot-7, 282-pound slugger―planted the Yankees’ Youth flag on the All-Star Weekend grounds by mashing four miles of dingers to take the crown, veteran Yankees clubhouse reporter Bryan Hoch looks back to the final days of Jeter's historic career, and then fleshes out general manager Brian Cashman’s blueprint for building a new-look Yankees roster, the young players’ fascinating paths to the Majors, their playoff run, streaks and slumps, historic assaults on the record books, how they stack up against Hall of Famers and Yankee legends, and whether or not they can maintain their alluring charisma and amazing numbers in the years to come. It’s a baseball insider’s account of how the Baby Bombers were born and how they’ve electrified Yankees Nation.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    This work includes strategies that provide teachers and teaching assistants with different perspectives, practical tools and the confidence for supporting these children.

    This text's applied approach addresses how and why a business operates. Students can then build upon their practical skills through examples, exercises and projects to help them gain a full understanding of how and why to develop a business plan. Each text is packaged with a Business Plan Booklet and CD-ROM, which are tied to end-of-part features, allowing students hands-on practice in creating a sound business plan. Through lively examples this text emphasizes key core skill areas including, Decision-Making and Planning, Teamwork, Technology, and Communication.

    Originally written to define the relation between the theories of transfinite numbers and mathematical games, the resulting work is a mathematically sophisticated but eminently enjoyable guide to game theory. By defining numbers as the strengths of positions in certain games, the author arrives at a new class, the surreal numbers, that includes both real numbers and ordinal numbers. These surreal numbers are applied in the author's mathematical analysis of game strategies. The additions to the Second Edition present recent developments in the area of mathematical game theory, with a concentration on surreal numbers and the additive theory of partizan games.

     There is no big league pitcher who is more respected for his skill than David Cone. In his stellar career Cone has won multiple championships andcountless professional accolades. Along the way, the perennial all-star has had to adjust to five different ballclubs, recover from a career-threatening arm aneurysm, cope with the lofty expectations that are standard for the games highest paid players, and overcome a humbling three-month, eight-game losing streak in the summer of 2000. Cone granted exclusive and unlimited access to baseballs most respected writer Roger Angell of the New Yorker. The result is just what baseball fans everywhere would expect from Angell: an extraordinary inside account of a superstar.

    

    From Gibson's early days in the Jim Crow South to his glory days as a World Series-winning pitcher, Stranger to the Game is the candid memoir of one of the game's greatest pitchers and most outspoken black players.

    With this book, you'll learn how to load data, assemble and disassemble data objects, navigate R's environment system, write your own functions, and use all of R's programming tools.RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You'll gain valuable programming skills and support your work as a data scientist at the same time.Work hands-on with three practical data analysis projects based on casino gamesStore, retrieve, and change data values in your computer's memoryWrite programs and simulations that outperform those written by typical R usersUse R programming tools such as if else statements, for loops, and S3 classesLearn how to write lightning-fast vectorized R codeTake advantage of R's package system and debugging toolsPractice and apply R programming concepts as you learn them

    The most popular applications-oriented approach to engineering technology fluid mechanics, this text covers all of the basic principles of fluid mechanics-both statics and dynamics-in a clear, practical presentation that ties theory directly to real devices and systems used in chemical process industries, manufacturing, plant engineering, waste water handling and product design. Readable and clearly written, the new 6th edition brings a much more attractive appearance to the book and includes many updates and additional features.

    He also speaks candidly about overcoming his lifelong addiction to alcohol, and the friends, family and thousands of fans who helped him do it.

    But this isn't the ability to which he attributes his success. Instead, Schmidt says his multi-million dollar achievements are due to his ability to apply old-fashioned business concepts to the game of poker. Six years into his career, Schmidt's resume is legendary: Nearly 10 million hands and 15,000 hours played. Mind-blowing win rates spanning massive sample sizes. $5 million won, and never a losing month. With "Treat Your Poker Like A Business," Schmidt teaches other online poker players to monetize their abilities as he did. He shows readers how to manage bankroll, rationalize variance, move up in stakes, avoid tilt, create new sources of revenue, and most importantly, become more profitable. Just as "Moneyball" did for baseball, "Treat Your Poker Like A Business" gives players an entirely new way to gain a competitive advantage.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.