Combinatorial Machine Learning

Providing a novel approach to sparsity, this comprehensive book presents the theory of stochastic processes that are ruled by linear stochastic differential equations, and that admit a parsimonious representation in a matched wavelet-like basis. Two key themes are the statistical property of infinite divisibility, which leads to two distinct types of behaviour - Gaussian and sparse - and the structural link between linear stochastic processes and spline functions, which is exploited to simplify the mathematical analysis. The core of the book is devoted to investigating sparse processes, including a complete description of their transform-domain statistics. The final part develops practical signal-processing algorithms that are based on these models, with special emphasis on biomedical image reconstruction. This is an ideal reference for graduate students and researchers with an interest in signal/image processing, compressed sensing, approximation theory, machine learning, or statistic

The principles of cognition are becoming increasingly important in the areas of signal processing, communications and control. In this groundbreaking book, Simon Haykin, a pioneer in the field and an award-winning researcher, educator and author, sets out the fundamental ideas of cognitive dynamic systems. Weaving together the various branches of study involved, he demonstrates the power of cognitive information processing and highlights a range of future research directions. The book begins with a discussion of core topics such as cognition and sensing, dealing, in particular, with the perception-action cycle. Bayesian filtering, machine learning and dynamic programming are then addressed. Building on these foundations, there is detailed coverage of two important practical applications, cognitive radar and cognitive radio. Blending theory and practice, this insightful book is aimed at all graduate students and researchers looking for a thorough grounding in this fascinating field.

Relational mathematics is to operations research and informatics what numerical mathematics is to engineering: it is intended to help modelling, reasoning, and computing. Its applications are therefore diverse, ranging from psychology, linguistics, decision aid, and ranking to machine learning and spatial reasoning. Although many developments have been made in recent years, they have rarely been shared amongst this broad community of researchers. This comprehensive 2010 overview begins with an easy introduction to the topic, assuming a minimum of prerequisites; but it is nevertheless theoretically sound and up to date. It is suitable for applied scientists, explaining all the necessary mathematics from scratch using a multitude of visualised examples, via matrices and graphs. It ends with tangible results on the research level. The author illustrates the theory and demonstrates practical tasks in operations research, social sciences and the humanities.

This book equips the reader to understand every important aspect of the dynamics of rotating machines. Will the vibration be large? What influences machine stability? How can the vibration be reduced? Which sorts of rotor vibration are the worst? The book develops this understanding initially using extremely simple models for each phenomenon, in which (at most) four equations capture the behavior. More detailed models are then developed based on finite element analysis, to enable the accurate simulation of the relevant phenomena for real machines. Analysis software (in MATLAB) is associated with this book, and novices to rotordynamics can expect to make good predictions of critical speeds and rotating mode shapes within days. The book is structured more as a learning guide than as a reference tome and provides readers with more than 100 worked examples and more than 100 problems and solutions.

Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.

Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.

Elementary Linear Algebra: Applications Version, 12th Edition, gives an elementary treatment of linear algebra that is suitable for a first course for undergraduate students. The classic treatment of linear algebra presents the fundamentals in the clearest possible way, examining basic ideas by means of computational examples and geometrical interpretation. It proceeds from familiar concepts to the unfamiliar, from the concrete to the abstract. Readers consistently praise this outstanding text for its expository style and clarity of presentation. In this edition, a new section has been added to describe the applications of linear algebra in emerging fields such as data science, machine learning, climate science, geomatics, and biological modeling. New exercises have been added with special attention to the expanded early introduction to linear transformations and new examples have been added, where needed, to support the exercise sets. Calculus is not a prerequisite, but there are clea

A HUMAN-CENTERED APPROACH TO ARTIFICIAL INTELLIGENCE WILL ENSURE HUMAN CONTROL OVER POWERFUL AND HELPFUL FUTURE MOBILE DEVICES AND SERVICESResearchers, developers, business leaders, policy makers, and others are expanding the technology-centered scope of artificial intelligence (AI) to include human-centered AI (HCAI) ways of thinking. This expansion from an algorithm-focused view to embrace a human-centered perspective can shape the future of technology to better serve human needs. The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities.However, as many technology companies and thought leaders have said, the goal is not to replace people, but to empower them by making design choices that give humans control over technology. In Human-Centered AI, Professor Ben Shneiderman aims to produce an optimistic, realistic, guide to how artificial intelligence can be used to augment and enhance humans' lives. This

Models and Applications of Tourists' Travel Behavior provides an overview of all possible approaches to modeling tourists' travel behavior, helping readers decide which theoretical approach should be chosen depending on the available type of data. It focuses on the connection between traditional travel behavior theories and tourist studies and introduces specific tourist contexts in travel demand modelling. It goes beyond the theoretical background of tourist travel behavior modeling and offers a practical understanding for choosing the right model and sourcing the right data. The book begins with the role of transport in tourist' travel behavior, then employs a literature review to establish the necessary background on the topic. It then goes on to describe theoretical approaches, descriptive approaches, and statistical approaches for modelling. It discusses choice models based on both Stated Preference Data and Revealed Preference Data. It concludes with chapters on machine learning

Finance students and practitioners may ask: can machines learn everything? Could AI help me? Computing students or practitioners may ask: which of my skills could contribute to finance? Where in finance should I pay attention? This book aims to answer these questions. No prior knowledge is expected in AI or finance.Including original research, the book explains the impact of ignoring computation in classical economics; examines the relationship between computing and finance and points out potential misunderstandings between economists and computer scientists; and introduces Directional Change and explains how this can be used.To finance students and practitioners, this book will explain the promise of AI, as well as its limitations. It will cover knowledge representation, modelling, simulation and machine learning, explaining the principles of how they work. To computing students and practitioners, this book will introduce the financial applications in which AI has made an impact. This

What happens to risk as the economic horizon goes to zero and risk is seen as an exposure to a change in state that may occur instantaneously at any time? All activities that have been undertaken statically at a fixed finite horizon can now be reconsidered dynamically at a zero time horizon, with arrival rates at the core of the modeling. This book, aimed at practitioners and researchers in financial risk, delivers the theoretical framework and various applications of the newly established dynamic conic finance theory. The result is a nonlinear non-Gaussian valuation framework for risk management in finance. Risk-free assets disappear and low risk portfolios must pay for their risk reduction with negative expected returns. Hedges may be constructed to enhance value by exploiting risk interactions. Dynamic trading mechanisms are synthesized by machine learning algorithms. Optimal exposures are designed for option positioning simultaneously across all strikes and maturities.

Marketing and sales prioritize AI and machine learning more than any other business department, yet often struggle with how to scale and strategize the opportunities it presents.AI Strategy for Sales and Marketing presents a framework for understanding how AI can boost customer-centricity and sales by creating a connected strategy that delivers value today and into the future. Supported by practical tips and advice throughout, it covers topics including personalization, upskilling, customer experience for both on and offline shopping channels and the importance of using AI responsibly to create consumer trust. Featuring original research and interviews with leading practitioners, it also contains global case studies from organizations in a range of sectors, including Samsung, LinkedIn, PwC, Rolls Royce and Hilton, with insights into the various stages of their adoption journeys. Written by a recognized industry expert, it is an invaluable resource for those wanting to benefit from usin

Marketing and sales prioritize AI and machine learning more than any other business department, yet often struggle with how to scale and strategize the opportunities it presents.AI Strategy for Sales and Marketing presents a framework for understanding how AI can boost customer-centricity and sales by creating a connected strategy that delivers value today and into the future. Supported by practical tips and advice throughout, it covers topics including personalization, upskilling, customer experience for both on and offline shopping channels and the importance of using AI responsibly to create consumer trust. Featuring original research and interviews with leading practitioners, it also contains global case studies from organizations in a range of sectors, including Samsung, LinkedIn, PwC, Rolls Royce and Hilton, with insights into the various stages of their adoption journeys. Written by a recognized industry expert, it is an invaluable resource for those wanting to benefit from usin

Artificial Intelligence-Aided Materials Design: AI-Algorithms and Case Studies on Alloys and Metallurgical Processes describes the application of artificial intelligence (AI)/machine learning (ML) concepts to develop predictive models that can be used to design alloy materials, including magnetic alloys, nickel-base superalloys, titanium-base alloys, and aluminum-base alloys. Readers new to AI/ML algorithms can use this book as a starting point and use the included MATLAB and Python implementation of AI/ML algorithms through included case studies. Experienced AI/ML researchers who want to try new algorithms can use this book and study the case studies for reference. Offers advantages and limitations of several AI concepts and their proper implementation in various data types generated through experiments and computer simulations and from industries in different file formats Helps readers develop predictive models through AI/ML algorithms by writing their own computer code or usin

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes wit

There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning.

Algorithms permeate our lives in numerous ways, performing tasks that until recently could only be carried out by humans. Artificial Intelligence (AI) technologies, based on machine learning algorithms and big-data-powered systems, can perform sophisticated tasks such as driving cars, analyzing medical data, and evaluating and executing complex financial transactions - often without active human control or supervision. Algorithms also play an important role in determining retail pricing, online advertising, loan qualification, and airport security. In this work, Martin Ebers and Susana Navas bring together a group of scholars and practitioners from across Europe and the US to analyze how this shift from human actors to computers presents both practical and conceptual challenges for legal and regulatory systems. This book should be read by anyone interested in the intersection between computer science and law, how the law can better regulate algorithmic design, and the legal ramificatio

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction

Classical computer science textbooks tell us that some problems are 'hard'. Yet many areas, from machine learning and computer vision to theorem proving and software verification, have defined their own set of tools for effectively solving complex problems. Tractability provides an overview of these different techniques, and of the fundamental concepts and properties used to tame intractability. This book will help you understand what to do when facing a hard computational problem. Can the problem be modelled by convex, or submodular functions? Will the instances arising in practice be of low treewidth, or exhibit another specific graph structure that makes them easy? Is it acceptable to use scalable, but approximate algorithms? A wide range of approaches is presented through self-contained chapters written by authoritative researchers on each topic. As a reference on a core problem in computer science, this book will appeal to theoreticians and practitioners alike.

Originating from the authors' own graduate course at the University of North Carolina, this material has been thoroughly tried and tested over many years, making the book perfect for a two-term course or for self-study. It provides a concise introduction that covers all of the measure theory and probability most useful for statisticians, including Lebesgue integration, limit theorems in probability, martingales, and some theory of stochastic processes. Readers can test their understanding of the material through the 300 exercises provided. The book is especially useful for graduate students in statistics and related fields of application (biostatistics, econometrics, finance, meteorology, machine learning, and so on) who want to shore up their mathematical foundation. The authors establish common ground for students of varied interests which will serve as a firm 'take-off point' for them as they specialize in areas that exploit mathematical machinery.

Originating from the authors' own graduate course at the University of North Carolina, this material has been thoroughly tried and tested over many years, making the book perfect for a two-term course or for self-study. It provides a concise introduction that covers all of the measure theory and probability most useful for statisticians, including Lebesgue integration, limit theorems in probability, martingales, and some theory of stochastic processes. Readers can test their understanding of the material through the 300 exercises provided. The book is especially useful for graduate students in statistics and related fields of application (biostatistics, econometrics, finance, meteorology, machine learning, and so on) who want to shore up their mathematical foundation. The authors establish common ground for students of varied interests which will serve as a firm 'take-off point' for them as they specialize in areas that exploit mathematical machinery.

Memory-based language processing - a machine learning and problem solving method for language technology - is based on the idea that the direct reuse of examples using analogical reasoning is more suited for solving language processing problems than the application of rules extracted from those examples. This book discusses the theory and practice of memory-based language processing, showing its comparative strengths over alternative methods of language modelling. Language is complex, with few generalizations, many sub-regularities and exceptions, and the advantage of memory-based language processing is that it does not abstract away from this valuable low-frequency information. By applying the model to a range of benchmark problems, the authors show that for linguistic areas ranging from phonology to semantics, it produces excellent results. They also describe TiMBL, a software package for memory-based language processing. The first comprehensive overview of the approach, this book wi

Inferring the precise locations and splicing patterns of genes in DNA is a difficult but important task, with broad applications to biomedicine. The mathematical and statistical techniques that have been applied to this problem are surveyed and organized into a logical framework based on the theory of parsing. Both established approaches and methods at the forefront of current research are discussed. Numerous case studies of existing software systems are provided, in addition to detailed examples that work through the actual implementation of effective gene-predictors using hidden Markov models and other machine-learning techniques. Background material on probability theory, discrete mathematics, computer science, and molecular biology is provided, making the book accessible to students and researchers from across the life and computational sciences. This book is ideal for use in a first course in bioinformatics at graduate or advanced undergraduate level, and for anyone wanting to kee

Inferring the precise locations and splicing patterns of genes in DNA is a difficult but important task, with broad applications to biomedicine. The mathematical and statistical techniques that have been applied to this problem are surveyed and organized into a logical framework based on the theory of parsing. Both established approaches and methods at the forefront of current research are discussed. Numerous case studies of existing software systems are provided, in addition to detailed examples that work through the actual implementation of effective gene-predictors using hidden Markov models and other machine-learning techniques. Background material on probability theory, discrete mathematics, computer science, and molecular biology is provided, making the book accessible to students and researchers from across the life and computational sciences. This book is ideal for use in a first course in bioinformatics at graduate or advanced undergraduate level, and for anyone wanting to kee

Advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the domain of ecologists are now being used more widely. This 2006 book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques. It aims to provide a framework that will enable the reader to recognise the assumptions and constraints that are implicit in all such techniques. Important generic issues are discussed first and then the major families of algorithms are described. Throughout the focus is on explanation and understanding and readers are directed to other resources that provide additional mathematical rigour when it is required. Examples taken from across the whole of biology, including bioinformatics, are provided throughout the book to illustrate the key concepts and each technique's potential.

Text mining is a new and exciting area of computer science research that tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. Similarly, link detection – a rapidly evolving approach to the analysis of text that shares and builds upon many of the key elements of text mining – also provides new tools for people to better leverage their burgeoning textual data resources. The Text Mining Handbook presents a comprehensive discussion of the state-of-the-art in text mining and link detection. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, the book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection in such varied fields a

A concise but informative overview of AI ethics and policy.Artificial intelligence, or AI for short, has generated a staggering amount of hype in the past several years. Is it the game-changer it's been cracked up to be? If so, how is it changing the game? How is it likely to affect us as customers, tenants, aspiring homeowners, students, educators, patients, clients, prison inmates, members of ethnic and sexual minorities, and voters in liberal democracies? Authored by experts in fields ranging from computer science and law to philosophy and cognitive science, this book offers a concise overview of moral, political, legal and economic implications of AI. It covers the basics of AI's latest permutation, machine learning, and considers issues such as transparency, bias, liability, privacy, and regulation.Both business and government have integrated algorithmic decision support systems into their daily operations, and the book explores the implications for our lives as citizens. For exam

The Strategy and Tactics of Pricing is the most well-established and influential strategic pricing text available, relied on by practitioners and students globally as a core guide for value-based pricing. The book explains how to balance the ability to create and extract value through from markets by managing pricing decisions in a more strategic and profitable manner. Rather than calculating prices to cover costs or to achieve sales goals, readers will learn to frame more strategic choices that proactively influence customer perceptions of value, manage internal costs, and profitably shift demand curves.This edition features new discussions on harnessing concepts from behavioral economics as well as a refined "value cascade" to help organize the topics covered in this book. Readers will also benefit from:1.Major revisions to more than a third of the chapters, including an expanded discussion of the role of artificial intelligence and machine learning analytics tools to assist in the e

Over the last half century, "smartness"―the drive for ubiquitous computing―has become a mandate: a new mode of managing and governing politics, economics, and the environment.Smart phones. Smart cars. Smart homes. Smart cities. The imperative to make our world ever smarter in the face of increasingly complex challenges raises several questions: What is this "smartness mandate?" How has it emerged, and what does it say about our evolving way of understanding―and managing―reality? How have we come to see the planet and its denizens first and foremost as data-collecting instruments? In The Smartness Mandate, Orit Halpern and Robert Mitchell radically suggest that "smartness" is not primarily a technology, but rather an epistemology. Through this lens, they offer a critical exploration of the practices, technologies, and subjects that such an understanding relies upon―above all, artificial intelligence and machine learning. The authors approach these not simply as techniques for solving pr

The entanglements of people and data, code and space, knowledge and power: how data and algorithms shape the world―and shape us within that world.With the emergence of a post-truth world, we have witnessed the dissolution of the common ground on which truth claims were negotiated, individual agency enacted, and public spheres shaped. What happens when, as Nietzsche claimed, there are no facts, but only interpretations? In this book, Mark Shepard examines the entanglements of people and data, code and space, knowledge and power that have produced an uncommon ground―a disaggregated public sphere where the extraction of behavioral data and their subsequent processing and sale have led to the emergence of micropublics of ever-finer granularity. Shepard explores how these new post-truth territories are propagated through machine learning systems and social networks, which shape the public and private spaces of everyday life. He traces the balkanization and proliferation of online news and t

An accessible analysis of the new forms of work whose seismic changes will increasingly determine the future of capitalism Automation and the decline in industrial employment have lead to rising fears of a workless future. But what happens when your work itself is the thing that will make your job obsolete? In the past few years, online crowdworking platforms - like Amazon's Mechanical Turk and Clickworker - have become an increasingly important source of work, particularly for those in the Global South. Here, small tasks are assigned to people online, and are often used to train algorithms to spot patterns, patterns through machine learning those same algorithms will then be able to spot more effectively than humans. Used for everything from the mechanics of self-driving cars to Google image search, this is an increasingly powerful part of the digital ecomomy. But what happens to work when it makes itself obsolete. In this stimulating work that blends political economy, studies of con

This book integrates the foundations of quantum computing with a hands-on coding approach to this emerging field; it is the first to bring these strands together in an updated manner. This work is suitable for both academic coursework and corporate technical training.The second edition includes extensive updates and revisions, both to textual content and to the code. Sections have been added on quantum machine learning, quantum error correction, Dirac notation and more. This new edition benefits from the input of the many faculty, students, corporate engineering teams, and independent readers who have used the first edition.This volume comprises three books under one cover: Part I outlines the necessary foundations of quantum computing and quantum circuits. Part II walks through the canon of quantum computing algorithms and provides code on a range of quantum computing methods in current use. Part III covers the mathematical toolkit required to master quantum computing. Additional reso

The increased and widespread availability of large network data resources in recent years has resulted in a growing need for effective methods for their analysis. The challenge is to detect patterns that provide a better understanding of the data. However, this is not a straightforward task because of the size of the data sets and the computer power required for the analysis. The solution is to devise methods for approximately answering the questions posed, and these methods will vary depending on the data sets under scrutiny. This cutting-edge text introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, before discussing the thought processes and creativity involved in the analysis of large-scale biological and medical data sets, using a wide range of real-life examples. Bringing together leading experts, this text provides an ideal introduction to and insight into the interdisciplinary field of network da

CELEBRATE FOOTBALL EVERY DAY OF THE YEAR WITH THE BRAND-NEW BOOK IN THE BESTSELLING SERIES WHERE FOOTBALL AND FACTS COME TOGETHER IN A DREAM TEAM FORMATION. THE PERFECT GIFT FOR ALL YOUNG FANS."The ultimate football yearbook where you can discover a new fun footie fact for every day of the year."-The Week JuniorWelcome to Football School Facts! It’s a book of facts and so let’s start with an important one: football is the best sport in the world.- Did you know that Erling Haaland is a record-breaking goalscoring machine?- That a player was once sent off for farting on the pitch?- Or that Diego Maradona has a religion named after him?Discover all this and much, much more all about your favourite players, great goals, super stadiums and marvellous mascots – the perfect gift for all young fans and their families to enjoy."An engaging, fun read that will keep children laughing, learning and dreaming of the pitch."- Women's Weekly"We love this series!"-Match of the Day Weekly***There’s a wh

Available in paperback! A young chapter book series starter about a chicken who loves math, science, and learning new things--by New York Times bestselling author Nancy Krulik. Perfect for fans of Unlimited Squirrels and Bumble and Bee. Chirpy is a young chicken stuck in the family coop. But she's not like the other chickens who just sit around peck peck pecking all day. She's intrigued by the children who come home from a place called school. They always look so happy! (Well, most days.) So Chirpy sneaks onto the bus one morning, hides in the classroom . . . and discovers a true love of math. When she shares what she's learned with the other chickens, they think she's one kernel short of a cob. Even so, Chirpy is determined to find a formula for fowl success by pecking away. But when a hungry fox tries to attack the coop, can she find the equation for a simple solution? With lively two-color art throughout, plus a fun hands-on simple machine activity, this eggs-ellent young chapter bo

近年來，人工智能與機器人在全世界範圍內備受關注。但是從2014年開始，國際權威調研機構高德納諮詢公司(Gartner)每年公佈的十大技術中，“大數據”這樣的詞語已經不再出現了。這不是因為“大數據”不再重要，而是意味著自2015年以後，“大數據”已經包含在十大技術裡，或者已經成為這些技術的基礎。“大數據”不僅可以用於構建和應用分析模型，還可以應用於機器學習(Machine Learning)、人工智能等諸多領域。本書從多種觀點出發，闡述了對大數據分析的理解和在環境、大數據企劃與設計層面上需要考慮的問題。

Implement real-time data processing applications on the Raspberry Pi. This book uniquely helps you work with data science concepts as part of real-time applications using the Raspberry Pi as a localized cloud. You'll start with a brief introduction to data science followed by a dedicated look at the fundamental concepts of Python programming. Here you'll install the software needed for Python programming on the Pi, and then review the various data types and modules available. The next steps are to set up your Pis for gathering real-time data and incorporate the basic operations of data science related to real-time applications. You'll then combine all these new skills to work with machine learning concepts that will enable your Raspberry Pi to learn from the data it gathers. Case studies round out the book to give you an idea of the range of domains where these concepts can be applied. By the end of Data Science with the Raspberry Pi, you'll understand that many applications are now de

這本書帶領讀者重新思考人工智慧的背後代價。不只是程式與演算法，AI的運作牽涉到龐大的自然資源、人力勞動與個人資料。作者凱特・克勞佛以十多年研究為基礎，揭示AI如何重新分配權力、加劇不平等，並對民主與環境造成深遠影響，是一部兼具深度與警示意義的作品。The hidden costs of artificial intelligence—from natural resources and labor to privacy, equality, and freedom“This study argues that [artificial intelligence] is neither artificial nor particularly intelligent. . . . A fascinating history of the data on which machine-learning systems are trained.”—New Yorker“A valuable corrective to much of the hype surrounding AI and a useful instruction manual for the future.”—John Thornhill, Financial Times“It’s a masterpiece, and I haven’t been able to stop thinking about it.”—Karen Hao, senior editor, MIT Tech ReviewWhat happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? Drawing on more than a decade of research, award‑winning scholar Kate Crawford reveals how AI is a technology of extraction: from the minerals drawn from

人工智慧（AI）將改變人類與所有產業的生態。 如果你對人工智慧，沒有全面的了解，那就跟不上時代了。很多影響人類的科技發展，都無法做出正確的判斷。 一個知名的投資專家說，比起5G，人人手上有，處處可見基地台，AI只有少數大公司用得上，到底有無潛力？有！但是不要想得太美了。 他說的對不對？很難判斷嗎？讀完本書，你就會找到正確的解答。 其實AI有三種，一種是雲端，就是連到那幾家大公司的機房，用他們大的嚇死人的算力。 一種是在本地運算，如汽車的自動駕駛。第三種則是運用在最前端的偵測裝置，以求最快的反應時間。 所以你說AI只有幾家大公司用得上，好像也沒有那麼對。 所以你要把它想得美一點，它是平地一聲雷，輝達、台積電的股價都應該後面加一千個零，是嗎？ 但是你知道有資訊科技以來，人類至少經過三次AI的浪潮。很多公司運用AI運用得嚇嚇叫，如文藝復興基金，用機器學習投資，20年來平均的年化報酬率遠勝巴菲特。 所以你說AI前程遠大！很OK，但它說他橫空出世？倒也未必，它只是一個連續的改善過程！ 當然有關AI最誇張的是想像是《魔鬼終結者》，一個是幹掉想毁滅人類的傢伙，而且他可以和人腦一樣的運作。不過有的人是殺人魔，有的人是慈善家。不知可以和人腦一樣運作的AI是慈善家或是殺人魔？ AI的形式有很多種，從撰寫大量複雜的函式來實現人工智慧的專家系統，到以大量資料反求智慧判定函式的演算法，如支援向量機（Support Vector Machine, SVM）、決策樹（Decision Tree）等，此領域統稱為機器學習（Machine Learning, ML），而運用神經網路技術，將網路的節點深度擴展延伸，以獲得更好的智慧判定效果，則稱為深度學習（Deep Learning, DL）。 你越了解AI的技術背景，就越能掌握它未來對人類的影響。如果你想投資AI相關產業，就更是需要深入了解了。 任何資訊科技的運用都有兩個層面，軟體以及執行軟體的硬體。本書除了說明AI運用的基本原理之外，也將詳細說明有關AI的軟體及硬體產業鏈。對一個投資人而言，完整的了解一個產業的上下游關係，是十分必要的。 總不能輝達業績創新高，而你完全沒有概念台積電會好還是不好吧？ 本書以圖文對照的方式，讓你對AI有清楚的認識。不論投資、或生活，可以在新科技浪潮中，穩定前行。 本書特色 ◎100個單元，

國內外最具代表性案例，9大專題實戰、15個分類實例 Google Colab、Microsoft Azure兩大雲端應用，人臉辨識、自然語言、文字識別、語音轉換、分析預測、物件自動標示、影像辦識真正實練，從資料收集整理、模型訓練調整，檢測修正到產出全面解秘！ 資料科學(Data Science)技術崛起後，人工智慧(Artificial Intelligence)、機器學習(Machine Learning)與深度學習(Deep Learning)儼然成為電腦科學最熱門的話題。其實，人工智慧的應用早已出現在我們生活周遭，如即時車牌影像辨識，以及智慧型手機中的臉部指紋辨識解鎖、影像轉文字翻譯、智慧語音助理…等。 在人工智慧領域中最重要也最適合作為入門的程式語言非Python莫屬。本書就以TensorFlow、Keras為基底，運用Python進行實作，深入人工智慧技術，掌握機器學習與深度學習的真正強大應用。 程式人、工程師挑戰人工智慧最佳學習地圖由類神經網路基礎到AI應用實戰訓練、模型、預測、辨識、分析與驗證全面深入機器學習與深度學習技術核心 ■打造專屬Python、TensorFlow與Keras最強開發環境。 ■圖解簡化複雜難懂的類神經網路觀念，涵蓋多層感知器(MLP)、卷積神經網路(CNN)與循環神經網路(RNN)。 ■整合最夯的人工智慧雲端開發平台：Google Colab，應用雲端提供的充沛資源，全面提升運算效能。 ■加碼最新的機器學習雲端應用平台：Microsoft Azure，連結功能強大、方向多元的Web API，豐富專業應用領域。 ■顛覆只重視理論或產生結果的傳統機器學習內容，實際挑戰從資料收集與準備、模型訓練與調整、檢測修正到結果產出的最完整機器學習實戰流程。 ■全面深入不同應用面向：印刷文字辨識、手寫文字辨識、遠端圖片分析、本機圖片分析、辨識圖片地標或名人、臉部辨識、人臉比對、語言識別、文字翻譯、黑白照片上色、人臉情緒偵測、圖片物件偵測、人工智慧製作縮圖、圖片場景偵測、汽車型號及年份偵測、自動標示物件、資料走勢預測… ■網羅國內外最具代表性案例：手寫文字辨識、圖片分類、即時匯率分析、文字雲與文章自動摘要、YouTube影片加上字幕、股票走勢分析、臉部辨識登入系統、擷取車牌、即時車牌影像辨識…等，進行有系統而扎實的真正演練。 ■一次領略機器學習與深