This text gives expanded explanations of the fundamentals of vibration including history of vibration, degree of freedom systems, vibration control, vibration measurement, and more. For engineers and other professionals who want a clear introduction to vibration engineering.

    It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpayd�n offers a concise and accessible overview of the new AI. This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpayd�n, author of a popular textbook on machine learning, explains that as Big Data has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data.

    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

    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.

    Airbnb. Amazon. Apple. PayPal. All of these companies disrupted their markets when they launched. Today they are industry leaders. What’s the secret to their success?These cutting-edge businesses are built on platforms: two-sided markets that are revolutionizing the way we do business. Written by three of the most sought-after experts on platform businesses, Platform Revolution is the first authoritative, fact-based book on platform models. Whether platforms are connecting sellers and buyers, hosts and visitors, or drivers with people who need a ride, Geoffrey G. Parker, Marshall W. Van Alstyne, and Sangeet Paul Choudary reveal the what, how, and why of this revolution and provide the first “owner’s manual” for creating a successful platform business.Platform Revolution teaches newcomers how to start and run a successful platform business, explaining ways to identify prime markets and monetize networks. Addressing current business leaders, the authors reveal strategies behind some of today’s up-and-coming platforms, such as Tinder and SkillShare, and explain how traditional companies can adapt in a changing marketplace. The authors also cover essential issues concerning security, regulation, and consumer trust, while examining markets that may be ripe for a platform revolution, including healthcare, education, and energy.As digital networks increase in ubiquity, businesses that do a better job of harnessing the power of the platform will win. An indispensable guide, Platform Revolution charts out the brilliant future of platforms and reveals how they will irrevocably alter the lives and careers of millions.

    Join them to explore a brave new world of brain-computer interfaces, vat-grown cruelty-free meat, knitted cars and guided bullets.The writers predict the vast changes that technology will bring to everything from food production to health care, energy output, manufacturing and the military balance. They also consider the impact on jobs, and how we can prepare for the opportunities, as well as the dangers, that await.Thought-provoking, engaging and full of insight from the forefront of tech innovation, Megatech is essential reading for anyone who wants to understand tomorrow's world.

    The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.Highlights a Range of Disciplines and ApplicationsDrawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge."

    These technologies so radically reshaped our world that we can no longer conceive of life without them.Today, the world’s population is projected to rise to well over 9.5 billion by 2050, and we are currently faced with the consequences of producing the energy that fuels, heats, and cools us. With temperatures and sea levels rising, and large portions of the globe plagued with drought, famine, and drug-resistant diseases, we need new technologies to tackle these problems.But we are on the cusp of a new convergence, argues world-renowned neuroscientist Susan Hockfield, with discoveries in biology coming together with engineering to produce another array of almost inconceivable technologies—next-generation products that have the potential to be every bit as paradigm shifting as the twentieth century’s digital wonders.The Age of Living Machines describes some of the most exciting new developments and the scientists and engineers who helped create them. Virus-built batteries. Protein-based water filters. Cancer-detecting nanoparticles. Mind-reading bionic limbs. Computer-engineered crops. Together they highlight the promise of the technology revolution of the twenty-first century to overcome some of the greatest humanitarian, medical, and environmental challenges of our time.

    The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it.In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go.Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.

    Soon, connected technology will be embedded in hundreds of everyday objects we already use: our cars, wallets, watches, umbrellas, even our trash cans. These objects will respond to our needs, come to know us, and learn to think on our behalf. David Rose calls these devices—which are just beginning to creep into the marketplace—Enchanted Objects.Some believe the future will look like more of the same—more smartphones, tablets, screens embedded in every conceivable surface. Rose has a different vision: technology that atomizes, combining itself with the objects that make up the very fabric of daily living. Such technology will be woven into the background of our environment, enhancing human relationships and channeling desires for omniscience, long life, and creative expression. The enchanted objects of fairy tales and science fiction will enter real life.Groundbreaking, timely, and provocative, Enchanted Objects is a blueprint for a better future, where efficient solutions come hand in hand with technology that delights our senses. It is essential reading for designers, technologists, entrepreneurs, business leaders, and anyone who wishes to understand the future and stay relevant in the Internet of Things.

    * Chapter on Verilog HDL allows for rapid start-up. * Illustrates the top-down design procedure used in modern VLSI chip design with an emphasis on variations in the HDL, logic, circuits and layout.

    The power of an artificial intelligence (AI) comes from its intelligence, not physical strength and laser guns. Humans steer the future not because we're the strongest or the fastest but because we're the smartest. When machines become smarter than humans, we'll be handing them the steering wheel. What promises—and perils—will these powerful machines present? Stuart Armstrong’s new book navigates these questions with clarity and wit.Can we instruct AIs to steer the future as we desire? What goals should we program into them? It turns out this question is difficult to answer! Philosophers have tried for thousands of years to define an ideal world, but there remains no consensus. The prospect of goal-driven, smarter-than-human AI gives moral philosophy a new urgency. The future could be filled with joy, art, compassion, and beings living worthwhile and wonderful lives—but only if we’re able to precisely define what a "good" world is, and skilled enough to describe it perfectly to a computer program.AIs, like computers, will do what we say—which is not necessarily what we mean. Such precision requires encoding the entire system of human values for an AI: explaining them to a mind that is alien to us, defining every ambiguous term, clarifying every edge case. Moreover, our values are fragile: in some cases, if we mis-define a single piece of the puzzle—say, consciousness—we end up with roughly 0% of the value we intended to reap, instead of 99% of the value.Though an understanding of the problem is only beginning to spread, researchers from fields ranging from philosophy to computer science to economics are working together to conceive and test solutions. Are we up to the challenge?A mathematician by training, Armstrong is a Research Fellow at the Future of Humanity Institute (FHI) at Oxford University. His research focuses on formal decision theory, the risks and possibilities of AI, the long term potential for intelligent life (and the difficulties of predicting this), and anthropic (self-locating) probability. Armstrong wrote Smarter Than Us at the request of the Machine Intelligence Research Institute, a non-profit organization studying the theoretical underpinnings of artificial superintelligence.

     Tim accepted, and after extensive meetings with Elon and his staff, he wrote four blog posts that Vox’s David Roberts called “the meatiest, most fascinating, most satisfying posts I've read in ages.” Here is the whole series, in one ebook.

    But they don't yet know what replaces it. As everyone and everything becomes more interconnected and digitized, how do you operate, compete, and win?Ming Zeng, the former Chief of Staff and strategy adviser to Alibaba Group's founder Jack Ma, explains how the latest technological developments, such as artificial intelligence, machine learning, the mobile internet, and cloud computing are redefining how value is created. Written especially for those outside the technology industry or the startup arena, this book introduces a simple, overarching framework to guide strategy formulation and execution in this data-rich and highly interactive environment.Revealing the revolutionary practices that he and his team have developed at Alibaba, Zeng shows how to: Automate decisions through machine learning Create products informed by real-time data from customers Determine the right strategic positioning to maximize value from platforms and suppliers Repurpose your organization to further human insight and enable creativity Lead your company's transformation into a smart businessWith insights into the strategies and tools used by leaders at Alibaba and other companies such as Ruhan and Red Collar, in a variety of industries from furniture making to banking to custom tailoring, Smart Business outlines a radically new approach to strategy that can be applied everywhe

    Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization’s massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you’ll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It’s a big, big data world out there – let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.