Rise of the Machines reclaims the spectacular story of cybernetics, one of the twentieth century’s pivotal ideas.Springing from the mind of mathematician Norbert Wiener amid the devastation of World War II, the cybernetic vision underpinned a host of seductive myths about the future of machines. Cybernetics triggered blissful cults and military gizmos, the Whole Earth Catalog and the air force’s foray into virtual space, as well as crypto-anarchists fighting for internet freedom.In Rise of the Machines, Thomas Rid draws on unpublished sources—including interviews with hippies, anarchists, sleuths, and spies—to offer an unparalleled perspective into our anxious embrace of technology.

    The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Added in several places including logical agents, planning, and natural language. *NEW-Increased coverage of material - Includes expanded coverage of: default reasoning and truth maintenance systems, including multi-agent/distributed AI and game theory; probabilistic approaches to learning including EM; more detailed descriptions of probabilistic inference algorithms. *NEW-Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises. *NEW-On-line Java software. *Makes it easy for students to do projects on the web using intelligent agents. *A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents. *Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making pa

    Numerous examples throughout provide readers with real-life applications of the concepts of analog and digital communications systems, while chapter-end questions and problems give them a chance to test and review their understanding of fundamental and key topics. Modern digital and data communications systems, microwave radio communications systems, satellite communications systems, and optical fiber communications systems. Cellular and PCS telephone systems coverage presents the latest and most innovative technological advancements being made in cellular communication systems. Optical fiber communications chapter includes new sections on light sources, optical power, optical sources and link budget. Current topics include trellis encoding, CCITT modem recommendations, PCM line speed, extended superframe format, wavelength division multiplexing, Kepler's laws, Clark orbits, limits of visibility, Satellite Radio Navigation and Navstar GPS. For the study of electronic communications systems.

    It is suitable for a one or two semester university course on AI, as well as for researchers in the field.

    It is one of egos and money, where millions of dollars are won and lost every day and phones are routinely thrown across hectic trading desks. This palpable excitement has led to the explosion of the retail FX market, which has unfortunately spawned a new breed of authors and gurus more than happy to provide misleading and often downright fraudulent information by promising traders riches while making forex trading 'easy'. Well I'll let you in on a little secret: there is nothing easy about trading currencies. If you don't believe me then stop by Warren Buffet's office and ask him how he could lose $850m betting on the dollar or ask George Soros why his short yen bets cost him $600m not once but twice in 1994. What's wrong with these guys, don't they read FX books? In reality, the average client's trading approach combined with the unscrupulous practices of some brokers make spot FX trading more akin to the games found on the Vegas strip than to anything seen on Wall St. The FX market is littered with the remains of day traders and genius 'systems, ' and to survive in the long-run traders have to realize that they are playing a game where the cards are clearly stacked against them. Have you ever had your stop hit at a price that turned out to be the low/high for the day? Bad luck perhaps? Maybe. What if it happens more than once? Do you ever feel like the market is out to get you? Well guess what, in this Zero Sum game it absolutely is. Covering the day-to-day mechanics of the FX market and the unsavoury dealings going on, Beat the Forex Dealer offers traders the market-proven trading techniques needed to side-step dealer traps and develop winning trading methods. Learn from an industry insider the truth behind dirty dealer practices including: stop-hunting, price shading, trading against clients and 'no dealing desk' realities. Detailing the dealer-inspired trading techniques developed by MIGFX Inc, consistently ranked among the world's leading currency trading firms, the book helps turn average traders into winning traders; and in a market with a 90% loss rate winning traders are in fact quite rare! More than just a simple manual, Beat the Forex Dealer brings to life the excitement of the FX market by delivering insights into some of the greatest trading triumphs and highlighting legendary disasters; all written in an easy to read style. Make no mistake about it there is a lot of money to be made in currency trading, you just have to know where to look. Sidestepping simple dealer traps is one way of improving your daily p&l, but it is surely not the only one. Successful trading comes down to taking care of the details, which means skipping the theoretical stuff and providing only up-to-date, real-life examples while sharing the FX trading tips that have proved so profitable over the years. By stripping away the theory and getting down to the core of trading, you too will find yourself on the way to beating the forex dealer!

    “Big data” refers to our burgeoning ability to crunch vast collections of information, analyze it instantly, and draw sometimes profoundly surprising conclusions from it. This emerging science can translate myriad phenomena—from the price of airline tickets to the text of millions of books—into searchable form, and uses our increasing computing power to unearth epiphanies that we never could have seen before. A revolution on par with the Internet or perhaps even the printing press, big data will change the way we think about business, health, politics, education, and innovation in the years to come. It also poses fresh threats, from the inevitable end of privacy as we know it to the prospect of being penalized for things we haven’t even done yet, based on big data’s ability to predict our future behavior.In this brilliantly clear, often surprising work, two leading experts explain what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. Big Data is the first big book about the next big thing.www.big-data-book.com

    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.

    I. is explored and explained in this best selling text. Assuming no prior knowledge, it covers topics like neural networks and robotics. This text explores the range of problems which have been and remain to be solved using A. I. tools and techniques. The second half of this text is an excellent reference.

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    

    It provides many detailed, practical design and analysis examples intended to relate theory to the workplace. Chapter topics include first experiences with an op amp; inverting and noninverting amplifiers; comparators and controls; selected applications of op amps; signal generators; op amps with diodes; differential, instrumentation, and bridge amplifiers; DC performance: bias, offsets, and drift; AC performance: bandwidth, slew rate, noise; active filters; modulating, demodulating, and frequency changing with the multiplier; integrated-circuit timers; digital-to-analog converters; analog-to-digital converters; and power supplies. For design engineers rs

    It’s easy and fun with this practical, hands-on introduction. Author Scott Murray teaches you the fundamental concepts and methods of D3, a JavaScript library that lets you express data visually in a web browser. Along the way, you’ll expand your web programming skills, using tools such as HTML and JavaScript.This step-by-step guide is ideal whether you’re a designer or visual artist with no programming experience, a reporter exploring the new frontier of data journalism, or anyone who wants to visualize and share data.Learn HTML, CSS, JavaScript, and SVG basicsDynamically generate web page elements from your data—and choose visual encoding rules to style themCreate bar charts, scatter plots, pie charts, stacked bar charts, and force-directed layoutsUse smooth, animated transitions to show changes in your dataIntroduce interactivity to help users explore data through different viewsCreate customized geographic maps with dataExplore hands-on with downloadable code and over 100 examples

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    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

    Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. This book can be used for readers who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.