Whether you are a student struggling to fulfill a math or science requirement, or you are embarking on a career change that requires a higher level of math competency, A Mind for Numbers offers the tools you need to get a better grasp of that intimidating but inescapable field. Engineering professor Barbara Oakley knows firsthand how it feels to struggle with math. She flunked her way through high school math and science courses, before enlisting in the army immediately after graduation. When she saw how her lack of mathematical and technical savvy severely limited her options—both to rise in the military and to explore other careers—she returned to school with a newfound determination to re-tool her brain to master the very subjects that had given her so much trouble throughout her entire life.
In A Mind for Numbers, Dr. Oakley lets us in on the secrets to effectively learning math and science—secrets that even dedicated and successful students wish they’d known earlier. Contrary to popular belief, math requires creative, as well as analytical, thinking. Most people think that there’s only one way to do a problem, when in actuality, there are often a number of different solutions—you just need the creativity to see them. For example, there are more than three hundred different known proofs of the Pythagorean Theorem. In short, studying a problem in a laser-focused way until you reach a solution is not an effective way to learn math. Rather, it involves taking the time to step away from a problem and allow the more relaxed and creative part of the brain to take over. A Mind for Numbers shows us that we all have what it takes to excel in math, and learning it is not as painful as some might think!

Combining insights from both cognitive neuroscience and molecular biology, two of the world's leading experts address memory from molecules and cells to brain systems and cognition. What is memory and where in the brain is it stored? How is memory storage accomplished? This book touches on these questions and many more, showing how the recent convergence of psychology and biology has resulted in an exciting new synthesis of knowledge about learning and remembering. Memory: From Mind to Molecules is an ideal primer for courses on learning and memory or for general readers who are interested in discovering what is currently known about one of the basic aspects of human existence.

Want to learn how to program in C++ immediately? Want to start writing better, more powerful C++ programs today? Accelerated C++'s uniquely modern approach will help you learn faster and more fluently than you ever believed possible. Based on the authors' intensive summer C++ courses at Stanford University, Accelerated C++ covers virtually every concept that most professional C++ programmers will ever use -- but it turns the "traditional" C++ curriculum upside down, starting with the high-level C++ data structures and algorithms that let you write robust programs immediately. Once you're getting results, Accelerated C++ takes you "under the hood," introducing complex language features such as memory management in context, and explaining exactly how and when to use them. From start to finish, the book concentrates on solving problems, rather than learning language and library features for their own sake. The result: You'll be writing real-world programs in no time -- and outstanding code faster than you ever imagined.

Bestselling Programming Tutorial and Reference Completely Rewritten for the New C++11 Standard
Now fully updated and recast for the newly released C++11 standard, this authoritative and comprehensive introduction to C++ will help you to learn the language fast, and to use it in modern, highly effective ways. Highlighting today’s best practices, the authors show how to use both the core language and its standard library to write code that is more efficient, readable, and powerful.
C++ Primer, Fifth Edition, introduces the C++ standard library from the outset, drawing on its common functions and facilities to help you write useful programs without first having to master every language detail. Examples take advantage of the library to explain language features and demonstrate how to make the best use of them. A proven tutorial for those new to C++, the authors’ authoritative discussion of core C++ concepts and techniques also make the book a valuable resource for experienced programmers, especially those eager to see C++11 enhancements illuminated.
Start Fast and Achieve More
* Learn how to use the C++11 Standard Library and new language features to quickly build robust programs, and get comfortable with high-level programming
* Learn through examples that illuminate today’s best coding styles and program design techniques
* Understand the “rationale behind the rules”: why C++11 works as it does
* Use the extensive cross-references to help you connect related concepts and insights
* Benefit from up-to-date learning aids and exercises that emphasize key points, help you to avoid pitfalls, promote good practices, and reinforce what you’ve learned
Access the source code for the extended examples as it becomes available at informit.com/title/0321714113.

Following his blockbuster biography of Steve Jobs, The Innovators is Walter Isaacson’s revealing story of the people who created the computer and the Internet. It is destined to be the standard history of the digital revolution and an indispensable guide to how innovation really happens.
What were the talents that allowed certain inventors and entrepreneurs to turn their visionary ideas into disruptive realities? What led to their creative leaps? Why did some succeed and others fail?
In his masterly saga, Isaacson begins with Ada Lovelace, Lord Byron’s daughter, who pioneered computer programming in the 1840s. He explores the fascinating personalities that created our current digital revolution, such as Vannevar Bush, Alan Turing, John von Neumann, J.C.R. Licklider, Doug Engelbart, Robert Noyce, Bill Gates, Steve Wozniak, Steve Jobs, Tim Berners-Lee, and Larry Page.
This is the story of how their minds worked and what made them so inventive. It’s also a narrative of how their ability to collaborate and master the art of teamwork made them even more creative.
For an era that seeks to foster innovation, creativity, and teamwork, The Innovators shows how they happen.

This 2008 book examines how multinational enterprises and international finance influenced the course of electrification around the world. Multinational enterprises played a crucial role in the spread of electric light and power from the 1870s through the first three decades of the twentieth century. However, their role did not persist, and by 1978 multinational enterprises in this sector had all but disappeared, replaced by electrical utility providers with national business structures. Yet, in recent years, there has been a vigorous revival. This book, a co-operative effort by the three authors and a group of experts from many countries, offers an analysis of the history of multinational enterprise. The authors take an integrated approach, not simply comparing national electrification experiences, but supplying a truly global account.

This 164-page book assembles and updates introductory white papers that have been available on our website. There are substantial new sections, including a 50-page glossary of terms, and two new chapters.
Paul Green (Professor Emeritus of Marketing, University of Pennsylvania) and the "father of conjoint analysis" wrote the foreword for Getting Started with Conjoint Analysis. He writes: "Getting Started with Conjoint Analysis is a practical no-nonsense guide to what happens when one designs, executes, and analyzes data from real marketplace problems. It should appeal to academics and consultant-practitioners alike. The book is easy to follow, while at the same time being almost encyclopedic in its coverage of topics ranging from study design to the presentation of results to clients."

In Making Things Happen, James Woodward develops a new and ambitious comprehensive theory of causation and explanation that draws on literature from a variety of disciplines and which applies to a wide variety of claims in science and everyday life. His theory is a manipulationist account, proposing that causal and explanatory relationships are relationships that are potentially exploitable for purposes of manipulation and control. This account has its roots in the commonsense idea that causes are means for bringing about effects; but it also draws on a long tradition of work in experimental design, econometrics, and statistics.
Woodward shows how these ideas may be generalized to other areas of science from the social scientific and biomedical contexts for which they were originally designed. He also provides philosophical foundations for the manipulationist approach, drawing out its implications, comparing it with alternative approaches, and defending it from common criticisms. In doing so, he shows how the manipulationist account both illuminates important features of successful causal explanation in the natural and social sciences, and avoids the counterexamples and difficulties that infect alternative approaches, from the deductive-nomological model onwards.
Making Things Happen will interest philosophers working in the philosophy of science, the philosophy of social science, and metaphysics, and as well as anyone interested in causation, explanation, and scientific methodology.

Drawing on an impressive roster of experts in the field, Fundamentals of Computer Graphics, Fourth Edition offers an ideal resource for computer course curricula as well as a user-friendly personal or professional reference.
Focusing on geometric intuition, the book gives the necessary information for understanding how images get onto the screen by using the complementary approaches of ray tracing and rasterization. It covers topics common to an introductory course, such as sampling theory, texture mapping, spatial data structure, and splines. It also includes a number of contributed chapters from authors known for their expertise and clear way of explaining concepts.
Highlights of the Fourth Edition Include:
Updated coverage of existing topics
Major updates and improvements to several chapters, including texture mapping, graphics hardware, signal processing, and data structures
A text now printed entirely in four-color to enhance illustrative figures of concepts
The fourth edition of Fundamentals of Computer Graphics continues to provide an outstanding and comprehensive introduction to basic computer graphic technology and theory. It retains an informal and intuitive style while improving precision, consistency, and completeness of material, allowing aspiring and experienced graphics programmers to better understand and apply foundational principles to the development of efficient code in creating film, game, or web designs.

David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the foundations and limitations of statistical modeling, illustrating basic arguments with examples from political science, public policy, law, and epidemiology. Freedman maintains that many new technical approaches to statistical modeling constitute not progress, but regress. Instead, he advocates a 'shoe leather' methodology, which exploits natural variation to mitigate confounding and relies on intimate knowledge of the subject matter to develop meticulous research designs and eliminate rival explanations. When Freedman first enunciated this position, he was met with scepticism, in part because it was hard to believe that a mathematical statistician of his stature would favor 'low-tech' approaches. But the tide is turning. Many social scientists now agree that statistical technique cannot substitute for good research design and subject matter knowledge. This book offers an integrated presentation of Freedman's views.

Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a selection of Donald B. Rubin's research articles on matched sampling, from the early 1970s, when the author was one of the major researchers involved in establishing the field, to recent contributions to this now extremely active area. The articles include fundamental theoretical studies that have become classics, important extensions, and real applications that range from breast cancer treatments to tobacco litigation to studies of criminal tendencies. They are organized into seven parts, each with an introduction by the author that provides historical and personal context and discusses the relevance of the work today. A concluding essay offers advice to investigators designing observational studies. The book provides an accessible introduction to the study of matched sampling and will be an indispensable reference for students and researchers.

This book defends the view that any adequate account of rational decision making must take a decision maker's beliefs about causal relations into account. The early chapters of the book introduce the nonspecialist to the rudiments of expected utility theory. The major technical advance offered by the book is a "representation theorem" that shows that both causal decision theory and its main rival, Richard Jeffrey's logic of decision, are both instances of a more general conditional decision theory. In providing the most complete and robust defense of causal decision theory the book will be of interest to a broad range of readers in philosophy, economics, psychology, mathematics, and artificial intelligence.

New! Java versions of all C++ programs in the book at http: //bit.ly/epi-programs Get a PDF sampler of EPI from http: //bit.ly/epi-sampler

Have you ever...

Wanted to work at an exciting futuristic company? Struggled with an interview problem that could have been solved in 15 minutes? Wished you could study real-world computing problems?

If so, you need to read Elements of Programming Interviews (EPI).

The core of EPI is a collection of 300 problems with detailed solutions, including over 100 figures and 250 tested programs. The problems are challenging, well-motivated, and accessible. They are representative of the questions asked at interviews at the most exciting companies.

The book begins with a summary of patterns for data structure, algorithms, and problem solving that will help you solve the most challenging interview problems. This is followed by chapters on basic and advanced data structures, algorithm design, concurrency, system design, probability and discrete mathematics. Each chapter starts with a brief review of key concepts and results followed by a deep and wide set of questions.

EPI concludes with a summary of the nontechnical aspects of interviewing, including common mistakes, strategies for a great interview, perspectives from across the table, negotiating the best offer, and much more.

"This book is the best compilation of programming related problems I have seen. It is a great resource for a diverse set of topics when preparing for technical interviews, as a quick refresher in a subject area or when you are just looking for a brain teaser to challenge yourself."
Shashank Gupta / Scaligent, formerly Engineering Manager, Amazon.com, Senior Engineering Manager, Yahoo!, Manager of Software Development, Cisco Systems

http://neuralnetworksanddeeplearning.com/

The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.