Gigabytes, exabytes (that’s one quintillion bytes) of data are sitting on servers across the world. So how can we start to access this explosion of information, this “big data,” and what can it tell us?   Erez Aiden and Jean-Baptiste Michel are two young scientists at Harvard who started to ask those questions. They teamed up with Google to create the Ngram Viewer, a Web-based tool that can chart words throughout the massive Google Books archive, sifting through billions of words to find fascinating cultural trends. On the day that the Ngram Viewer debuted in 2010, more than one million queries were run through it.   On the front lines of Big Data, Aiden and Michel realized that this big dataset—the Google Books archive that contains remarkable information on the human experience—had huge implications for looking at our shared human history. The tool they developed to delve into the data has enabled researchers to track how our language has evolved over time, how art has been censored, how fame can grow and fade, how nations trend toward war. How we remember and how we forget. And ultimately, how Big Data is changing the game for the sciences, humanities, politics, business, and our culture.

    Sousa discusses the cognitive mechanisms for learning mathematics and the environmental and developmental factors that contribute to mathematics difficulties. This award-winning text examines:Children's innate number sense and how the brain develops an understanding of number relationships Rationales for modifying lessons to meet the developmental learning stages of young children, preadolescents, and adolescents How to plan lessons in PreK-12 mathematics Implications of current research for planning mathematics lessons, including discoveries about memory systems and lesson timing Methods to help elementary and secondary school teachers detect mathematics difficulties Clear connections to the NCTM standards and curriculum focal points

    In this unconventional introduction, physicist Leonard Susskind and hacker-scientist George Hrabovsky offer a first course in physics and associated math for the ardent amateur. Unlike most popular physics books—which give readers a taste of what physicists know but shy away from equations or math—Susskind and Hrabovsky actually teach the skills you need to do physics, beginning with classical mechanics, yourself. Based on Susskind's enormously popular Stanford University-based (and YouTube-featured) continuing-education course, the authors cover the minimum—the theoretical minimum of the title—that readers need to master to study more advanced topics.An alternative to the conventional go-to-college method, The Theoretical Minimum provides a tool kit for amateur scientists to learn physics at their own pace.

    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

    

    Many designers and researchers view usability and design as qualitative activities, which do not require attention to formulas and numbers. However, usability practitioners and user researchers are increasingly expected to quantify the benefits of their efforts. The impact of good and bad designs can be quantified in terms of conversions, completion rates, completion times, perceived satisfaction, recommendations, and sales.The book discusses ways to quantify user research; summarize data and compute margins of error; determine appropriate samples sizes; standardize usability questionnaires; and settle controversies in measurement and statistics. Each chapter concludes with a list of key points and references. Most chapters also include a set of problems and answers that enable readers to test their understanding of the material. This book is a valuable resource for those engaged in measuring the behavior and attitudes of people during their interaction with interfaces.

    Chain mail, also known as chainmail, maille, or chainmaille is a great hobby for any age that doesn't involve just making armor.You can make almost anything from chainmail. Below are just a few examples of what you can make:• Jewerly (Bracelets, Necklaces, Anklets, Earrings, Rings)• Clothing (Bikini tops, Belts, Shirts, Ties)• Art• Armor• Inlays (pictures in your maille)• And much, much more!This introductory volume focuses on getting you up and chainmailing as fast as possible. It’ll zero in on the absolute minimum you’ll need to start weaving within 24 hours as outlined below:• The tools and materials you’ll need• Wrapping wire into coils with a coiling jig you’ll build• Cutting rings from the coils you wound• Weaving one of the most common and easiest weaves from detailed graphic instructions• Practicing the weave learned with three different exercises• Learning the European or Euro 4-in-1 weave• An elegant bracelet project for women or men Chainmail is neither difficult to learn nor expensive. All it takes is a willingness to try something new no matter your age, education, or income! Scoll up and click the "Look Inside" feature on the top left hand side of this page to see what's included in this book.A Personal Note From The Author:This introductory volume blends 10 years of my personal experience chainmailling. When I first started chainmailling, I knew nothing about it except what I'd seen online or in movies; chainmail armor. Not very exciting for the average person but the idea of weaving metal rings into something I could create without any special skills appealed to me. I stumbled across a website with instructions and rings for sale and I've been hooked ever since!

    The system incorporates many of the productivity concepts made famous by David Allen in his book Getting Things Done (GTD). The author provides additional support through a website and Youtube videos. Download the book today to take back control of your Inbox.Note: the concepts in the book apply to Microsoft Outlook for Windows or Mac. They do not apply for outlook.com, but I am working on some workarounds to apply some of the concepts to the web version of Outlook.

    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.

    It features the twenty-five breeds that most breeders and dog fanciers agree constitute the country’s canine heritage. Divided into three groupings—working dogs, companion dogs and hounds—the book provides detailed background notes to each breed, along with information on its physical characteristics, behaviour, uses, origins and history. Along with popular breeds like Caravan hounds (or Karuvanis), Chippiparais, Mudhol hounds, Pashmis, Rajapalayams and Rampur hounds the book also features lesser known breeds such as the Alaknoori and the Jonangi. The fruit of several years of travel and research into India’s dog breeds, as well as S. Theodore Baskaran’s hands-on experience of raising various dogs, this celebration of our dogs is a book that no dog lover can do without.

    It is designed as a reference for students in the physical sciences and engineering.

    The authors take time to explain statistical procedures so that you can go beyond memorizing formulas and gain a conceptual understanding of statistics. The authors also take care to show you how having an understanding of statistical procedures will help you comprehend published findings and will lead you to become a savvy consumer of information. Known for its exceptional accuracy and examples, this text also has a complete supplements package to support your learning.

    You can live in an architectural masterpiece, but if you can’t see the floor, it is hard to appreciate it. How to Organize Your Life (At Home) takes you room-by-room through your house and shows you how to organize and enjoy your home again.The first book in this series, How to Organize Your Life (Every Day), provided detailed steps for organizing your daily routine. This sequel helps you bring that kind of order to your home space. Both books are for people who want to live an organized life, but are not sure exactly where to start.How to Organize Your Life (At Home) also includes tips for people who live in small spaces, have limited time, or have roommates. So whether you own a mansion or rent a room, this book can help you take your home from chaos to order.

    Imagine that you face three doors, behind one of which is a prize. You choose one but do not open it. The host--call him Monty Hall--opens a different door, alwayschoosing one he knows to be empty. Left with two doors, will you do better by sticking with your first choice, or by switching to the other remaining door? In this light-hearted yet ultimately serious book, Jason Rosenhouse explores the history of this fascinating puzzle. Using a minimum ofmathematics (and none at all for much of the book), he shows how the problem has fascinated philosophers, psychologists, and many others, and examines the many variations that have appeared over the years. As Rosenhouse demonstrates, the Monty Hall Problem illuminates fundamental mathematical issuesand has abiding philosophical implications. Perhaps most important, he writes, the problem opens a window on our cognitive difficulties in reasoning about uncertainty.

    It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpayd�n offers a concise and accessible overview of the new AI. This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpayd�n, author of a popular textbook on machine learning, explains that as Big Data has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data.