This book provides an applications-oriented introduction to multivariate data analysis for the non-statistician, by focusing on the fundamental concepts that affect the use of specific techniques.

    A fleet surrounded. One AI's struggle to save his machine brothers and sisters--and humanity itself--from complete destruction.Jain and his fleet of elite AI warships hurry to Earth's defense when an alien empire known as the Link stages an all-out attack. Though his fleet is equipped with the most powerful weapons tech ever developed, with enough firepower to raze an entire moon base, even Jain's mighty vessels prove no match for the enemy. When unexpected allies show up, the tide seems to turn in his favor. But will it be enough? Perhaps. The aliens were expecting to find a race of organics barely out of the stone ages.It must have come as quite a shock when they realized they faced a budding AI Empire.

    Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

    When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution.Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.

    The book will teach you about:* Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data* Deep learning, a powerful set of techniques for learning in neural networksNeural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.

    In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect.Since the dawn of the digital age, decision-making in finance, employment, politics, health and human services has undergone revolutionary change. Today, automated systems—rather than humans—control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor.In Automating Inequality, Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lays dying to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile.The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhumane choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In the process, they weaken democracy and betray our most cherished national values.This deeply researched and passionate book could not be more timely.Naomi Klein: "This book is downright scary."Ethan Zuckerman, MIT: "Should be required reading."Dorothy Roberts, author of Killing the Black Body: "A must-read for everyone concerned about modern tools of inequality in America."Astra Taylor, author of The People's Platform: "This is the single most important book about technology you will read this year."

    A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.

    In fact, his main concern is "How long until the government finds me?" Now the owner of a Mobile Network Droid and enrolled as a beta-tester for a brand new extraterrestrial package delivery system, Zane Pendon and Rob, a self-aware advertising drone, have to navigate the intricacies of shopping at home while avoiding arousing any suspicion from the Solar Alliance of Independent Planets and the US government, both of which are very interested in recovering the wayward piece of technology. However, it won't be long before worlds collide and mankind meets alien life. But with their status as intergalactic fugitives, Zane and Rob have their work cut out for them trying to stay alive and under the radar. Can alien infomercials save the world? Of course not. But they just might save Zane and his family.

    This comprehensive work covers both statistical and symbolic approaches to language processing; it shows how they can be applied to important tasks such as speech recognition, spelling and grammar correction, information extraction, search engines, machine translation, and the creation of spoken-language dialog agents. The following distinguishing features make the text both an introduction to the field and an advanced reference guide.- UNIFIED AND COMPREHENSIVE COVERAGE OF THE FIELDCovers the fundamental algorithms of each field, whether proposed for spoken or written language, whether logical or statistical in origin.- EMPHASIS ON WEB AND OTHER PRACTICAL APPLICATIONSGives readers an understanding of how language-related algorithms can be applied to important real-world problems.- EMPHASIS ON SCIENTIFIC EVALUATIONOffers a description of how systems are evaluated with each problem domain.- EMPERICIST/STATISTICAL/MACHINE LEARNING APPROACHES TO LANGUAGE PROCESSINGCovers all the new statistical approaches, while still completely covering the earlier more structured and rule-based methods.

    Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

     Data Structures and Algorithms in Python is the first mainstream object-oriented book available for the Python data structures course. Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++.

    Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problemsEach chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer supportThe authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data miningMore advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data miningCovers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis

    This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. Time Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results.The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.-- "Journal of Economics"

    This volume is a theoretical and practical approach to the design of computer technology.

    Subsequent sections deal with integrating factors; dilution and accretion problems; linearization of first order systems; Laplace Transforms; Newton's Interpolation Formulas, more.