With the advent of the Internet and the limitless information it contains, we're less sure about what we know, who knows what, or even what it means to know at all. And yet, human knowledge has recently grown in previously unimaginable ways and in inconceivable directions. In Too Big to Know, David Weinberger explains that, rather than a systemic collapse, the Internet era represents a fundamental change in the methods we have for understanding the world around us. With examples from history, politics, business, philosophy, and science, Too Big to Know describes how the very foundations of knowledge have been overturned, and what this revolution means for our future.

Latent Variable Models: An Introduction to Factor, Path, and Structural Equation
Analysis introduces latent variable models by utilizing path diagrams to explain the
relationships in the models. This approach helps less mathematically-inclined readers to grasp the underlying relations among path analysis, factor analysis, and structural equation modeling, and to set up and carry out such analyses. This revised and expanded fifth edition again contains key chapters on path analysis, structural equation models, and exploratory factor analysis. In addition, it contains new material on composite reliability, models with categorical data, the minimum average partial procedure, bi-factor models, and communicating about latent variable models.
The informal writing style and the numerous illustrative examples make the book
accessible to readers of varying backgrounds. Notes at the end of each chapter
expand the discussion and provide additional technical detail and references. Moreover, most chapters contain an extended example in which the authors work through one of the chapter’s examples in detail to aid readers in conducting similar analyses with their own data. The book and accompanying website provide all of the data for the book’s examples as well as syntax from latent variable programs so readers can replicate the analyses. The book can be used with any of a variety of computer programs, but special attention is paid to LISREL and R.
An important resource for advanced students and researchers in numerous disciplines in the behavioral sciences, education, business, and health sciences, Latent Variable Models is a practical and readable reference for those seeking to understand or conduct an analysis using latent variables.

In The Mind within the Brain, David Redish brings together cutting edge research in psychology, robotics, economics, neuroscience, and the new fields of neuroeconomics and computational psychiatry, to offer a unified theory of human decision-making. Most importantly, Redish shows how vulnerabilities, or "failure-modes," in the decision-making system can lead to serious dysfunctions, such as irrational behavior, addictions, problem gambling, and PTSD. Told with verve and humor in an easily readable style, Redish makes these difficult concepts understandable. Ranging widely from the surprising roles of emotion, habit, and narrative in decision-making, to the larger philosophical questions of how mind and brain are related, what makes us human, the nature of morality, free will, and the conundrum of robotics and consciousness, The Mind within the Brain offers fresh insight into one of the most complex aspects of human behavior.

Bayesian nets are widely used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. But many philosophers have criticised and ultimately rejected the central assumption on which such work is based - the Causal Markov Condition. So should Bayesian nets be abandoned? What explains their success in artificial intelligence? This book argues that the Causal Markov Condition holds as a default rule: it often holds but may need to be repealed in the face of counterexamples. Thus Bayesian nets are the right tool to use by default but naively applying them can lead to problems. The book develops a systematic account of causal reasoning and shows how Bayesian nets can be coherently employed to automate the reasoning processes of an artificial agent. The resulting framework for causal reasoning involves not only new algorithms but also new conceptual foundations. Probability and causality are treated as mental notions - part of an agent's belief state.Yet probability and causality are also objective - different agents with the same background knowledge ought to adopt the same or similar probabilistic and causal beliefs. This book, aimed at researchers and graduate students in computer science, mathematics and philosophy, provides a general introduction to these philosophical views as well as an exposition of the computational techniques that they motivate.

This 25th anniversary edition of Steven Levy's classic book traces the exploits of the computer revolution's original hackers -- those brilliant and eccentric nerds from the late 1950s through the early '80s who took risks, bent the rules, and pushed the world in a radical new direction. With updated material from noteworthy hackers such as Bill Gates, Mark Zukerberg, Richard Stallman, and Steve Wozniak, Hackers is a fascinating story that begins in early computer research labs and leads to the first home computers. Levy profiles the imaginative brainiacs who found clever and unorthodox solutions to computer engineering problems. They had a shared sense of values, known as "the hacker ethic," that still thrives today. Hackers captures a seminal period in recent history when underground activities blazed a trail for today's digital world, from MIT students finagling access to clunky computer-card machines to the DIY culture that spawned the Altair and the Apple II.

Amazon.com Exclusive: The Rant Heard Round the World
By Steven Levy

Author Steven Levy When I began researching Hacker s--so many years ago that it’s scary--I thought I’d largely be chronicling the foibles of a sociologically weird cohort who escaped normal human interaction by retreating to the sterile confines of computers labs. Instead, I discovered a fascinating, funny cohort who wound up transforming human interaction, spreading a culture that affects our views about everything from politics to entertainment to business. The stories of those amazing people and what they did is the backbone of Hackers: Heroes of the Computer Revolution .

But when I revisited the book recently to prepare the 25th Anniversary Edition of my first book, it was clear that I had luckily stumbled on the origin of a computer (and Internet) related controversy that still permeates the digital discussion. Throughout the book I write about something I called The Hacker Ethic, my interpretation of several principles implicitly shared by true hackers, no matter whether they were among the early pioneers from MIT’s Tech Model Railroad Club (the Mesopotamia of hacker culture), the hardware hackers of Silicon Valley’s Homebrew Computer Club (who invented the PC industry), or the slick kid programmers of commercial game software. One of those principles was “Information Should Be Free.” This wasn’t a justification of stealing, but an expression of the yearning to know more so one could hack more. The programs that early MIT hackers wrote for big computers were stored on paper tapes. The hackers would keep the tapes in a drawer by the computer so anyone could run the program, change it, and then cut a new tape for the next person to improve. The idea of ownership was alien.
This idea came under stress with the advent of personal computers. The Homebrew Club was made of fanatic engineers, along with a few social activists who were thrilled at the democratic possibilities of PCs. The first home computer they could get their hands on was 1975’s Altair, which came in a kit that required a fairly hairy assembly process. (Its inventor was Ed Roberts, an underappreciated pioneer who died earlier this year.) No software came with it. So it was a big deal when 19-year-old Harvard undergrad Bill Gates and his partner Paul Allen wrote a BASIC computer language for it. The Homebrew people were delighted with Altair BASIC, but unhappy that Gates and Allen charged real money for it. Some Homebrew people felt that their need for it outweighed their ability to pay. And after one of them got hold of a “borrowed” tape with the program, he showed up at a meeting with a box of copies (because it is so easy to make perfect copies in the digital age), and proceeded to distribute them to anyone who wanted one, gratis.
This didn’t sit well with Bill Gates, who wrote what was to become a famous “Letter to Hobbyists,” basically accusing them of stealing his property. It was the computer-age equivalent to Luther posting the Ninety-Five Theses on the Castle Church. Gate’s complaints would reverberate well into the Internet age, and variations on the controversy persist. Years later, when another undergrad named Shawn Fanning wrote a program called Napster that kicked off massive piracy of song files over the Internet, we saw a bloodier replay of the flap. Today, issues of cost, copying and control still rage--note Viacom’s continuing lawsuit against YouTube and Google. And in my own business—journalism--availability of free news is threatening more traditional, expensive new-gathering. Related issues that also spring from controversies in Hackers are debates over the “walled gardens” of Facebook and Apple’s iPad.
I ended the original Hackers with a portrait of Richard Stallman, an MIT hacker dedicated to the principle of free software. I recently revisited him while gathering new material for the 25th Anniversary Edition of Hackers , he was more hard core than ever. He even eschewed the Open Source movement for being insufficiently noncommercial.
When I spoke to Gates for the update, I asked him about his 1976 letter and the subsequent intellectual property wars. “Don’t call it war,” he said. “Thank God we have an incentive system. Striking the right balance of how this should work, you know, there's going to be tons of exploration.” Then he applied the controversy to my own situation as a journalism. “Things are in a crazy way for music and movies and books,” he said. “Maybe magazine writers will still get paid 20 years from now. Who knows? Maybe you'll have to cut hair during the day and just write articles at night.”
So Amazon.com readers, it’s up to you. Those who have not read Hackers, , have fun and be amazed at the tales of those who changed the world and had a hell of time doing it. Those who have previously read and loved Hackers , replace your beat-up copies, or the ones you loaned out and never got back, with this beautiful 25th Anniversary Edition from O’Reilly with new material about my subsequent visits with Gates, Stallman, and younger hacker figures like Mark Zuckerberg of Facebook. If you don’t I may have to buy a scissors--and the next bad haircut could be yours! Read Bill Gates' letter to hobbyists

"Nate Silver's The Signal and the Noise is The Soul of a New Machine for the 21st century."
—Rachel Maddow, author of Drift
Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. The New York Times now publishes FiveThirtyEight.com, where Silver is one of the nation’s most influential political forecasters.
Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future.
In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good—or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science.
Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise.
With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read.

The design of trading algorithms requires sophisticated mathematical models backed up by reliable data. In this textbook, the authors develop models for algorithmic trading in contexts such as executing large orders, market making, targeting VWAP and other schedules, trading pairs or collection of assets, and executing in dark pools. These models are grounded on how the exchanges work, whether the algorithm is trading with better informed traders (adverse selection), and the type of information available to market participants at both ultra-high and low frequency. Algorithmic and High-Frequency Trading is the first book that combines sophisticated mathematical modelling, empirical facts and financial economics, taking the reader from basic ideas to cutting-edge research and practice. If you need to understand how modern electronic markets operate, what information provides a trading edge, and how other market

This popular text provides an accessible guide to the application, interpretation, and pitfalls of structural equation modeling (SEM). Reviewed are fundamental statistical concepts--such as correlation, regressions, data preparation and screening, path analysis, and confirmatory factor analysis--as well as more advanced methods, including the evaluation of nonlinear effects, measurement models and structural regression models, latent growth models, and multilevel SEM. Special features include a Web page offering data and program syntax files for many of the research examples, electronic overheads that can be downloaded and printed by instructors or students, and links to SEM-related resources.

This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.

This book is aimed to provide an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria: 1) expertise or knowledge of the authors; 2) the application areas that have already been transformed by the successful use of deep learning technology, such as speech recognition and computer vision; and 3) the application areas that have the potential to be impacted significantly by deep learning and that have gained concentrated research efforts, including natural language and text processing, information retrieval, and multimodal information processing empowered by multi-task deep learning.
In Chapter 1, we provide the background of deep learning, as intrinsically connected to the use of multiple layers of nonlinear transformations to derive features from the sensory signals such as speech and visual images. In the most recent literature, deep learning is embodied also as representation learning, which involves a hierarchy of features or concepts where higher-level representations of them are defined from lower-level ones and where the same lower-level representations help to define higher-level ones. In Chapter 2, a brief historical account of deep learning is presented. In particular, selected chronological development of speech recognition is used to illustrate the recent impact of deep learning that has become a dominant technology in speech recognition industry within only a few years since the start of a collaboration between academic and industrial researchers in applying deep learning to speech recognition. In Chapter 3, a three-way classification scheme for a large body of work in deep learning is developed. We classify a growing number of deep learning techniques into unsupervised, supervised, and hybrid categories, and present qualitative descriptions and a literature survey for each category. From Chapter 4 to Chapter 6, we discuss in detail three popular deep networks and related learning methods, one in each category. Chapter 4 is devoted to deep autoencoders as a prominent example of the unsupervised deep learning techniques. Chapter 5 gives a major example in the hybrid deep network category, which is the discriminative feed-forward neural network for supervised learning with many layers initialized using layer-by-layer generative, unsupervised pre-training. In Chapter 6, deep stacking networks and several of the variants are discussed in detail, which exemplify the discriminative or supervised deep learning techniques in the three-way categorization scheme.
In Chapters 7-11, we select a set of typical and successful applications of deep learning in diverse areas of signal and information processing and of applied artificial intelligence. In Chapter 7, we review the applications of deep learning to speech and audio processing, with emphasis on speech recognition organized according to several prominent themes. In Chapters 8, we present recent results of applying deep learning to language modeling and natural language processing. Chapter 9 is devoted to selected applications of deep learning to information retrieval including Web search. In Chapter 10, we cover selected applications of deep learning to image object recognition in computer vision. Selected applications of deep learning to multi-modal processing and multi-task learning are reviewed in Chapter 11. Finally, an epilogue is given in Chapter 12 to summarize what we presented in earlier chapters and to discuss future challenges and directions.

Now in the 4th edition, Cracking the Coding Interview gives you the interview preparation you need to get the top software developer jobs. This book provides:
* 150 Programming Interview Questions and Solutions: From binary trees to binary search, this list of 150 questions includes the most common and most useful questions in data structures, algorithms, and knowledge based questions.
* Ten Mistakes Candidates Make -- And How to Avoid Them: Don't lose your dream job by making these common mistakes. Learn what many candidates do wrong, and how to avoid these issues.
* Steps to Prepare for Behavioral and Technical Questions: Stop meandering through an endless set of questions, while missing some of the most important preparation techniques. Follow these steps to more thoroughly prepare in less time.
* Interview War Stories: A View from the Interviewer's Side: Humorous but instructive stories from our interviewers show you how some candidates really flopped on the most important question - and how you can avoid doing the same.