Focused on helping students understand and construct proofs and expanding their mathematical maturity, this best-selling text is an accessible introduction to discrete mathematics. Johnsonbaugh's algorithmic approach emphasizes problem-solving techniques. The Seventh Edition reflects user and reviewer feedback on both content and organization.

    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.

    A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow.

    The book drives to the heart of the exciting debate on the origins of life and maintenance of order in complex biological systems. It focuses on the concept of self-organization: the spontaneous emergence of order that is widely observed throughout nature Kauffman argues that self-organization plays an important role in the Darwinian process of natural selection. Yet until now no systematic effort has been made to incorporate the concept of self-organization into evolutionary theory. The construction requirements which permit complex systems to adapt are poorly understood, as is the extent to which selection itself can yield systems able to adapt more successfully. This book explores these themes. It shows how complex systems, contrary to expectations, can spontaneously exhibit stunning degrees of order, and how this order, in turn, is essential for understanding the emergence and development of life on Earth. Topics include the new biotechnology of applied molecular evolution, with its important implications for developing new drugs and vaccines; the balance between order and chaos observed in many naturally occurring systems; new insights concerning the predictive power of statistical mechanics in biology; and other major issues. Indeed, the approaches investigated here may prove to be the new center around which biological science itself will evolve. The work is written for all those interested in the cutting edge of research in the life sciences.

    Scientists have marveled at the beauty and elegance of his analysis, while ordinary programmers have successfully applied his "cookbook" solutions to their day-to-day problems. All have admired Knuth for the breadth, clarity, accuracy, and good humor found in his books. "I can't begin to tell you how many pleasurable hours of study and recreation they have afforded me I have pored over them in cars, restaurants, at work, at home... and even at a Little League game when my son wasn't in the line-up.""--"Charles Long Primarily written as a reference, some people have nevertheless found it possible and interesting to read each volume from beginning to end. A programmer in China even compared the experience to reading a poem. "If you think you're a really good programmer... read Knuth's] "Art of Computer Programming.".. You should definitely send me a resume if you can read the whole thing.""--"Bill Gates Whatever your background, if you need to do any serious computer programming, you will find your own good reason to make each volume in this series a readily accessible part of your scholarly or professional library. "It's always a pleasure when a problem is hard enough that you have to get the Knuths off the shelf. I find that merely opening one has a very useful terrorizing effect on computers.""--"Jonathan LaventholIn describing the new fourth volume, one reviewer listed the qualities that distinguish all of Knuth's work. In sum: ] "detailed coverage of the basics, illustrated with well-chosen examples; occasional forays into more esoteric topics and problems at the frontiers of research; impeccable writing peppered with occasional bits of humor; extensive collections of exercises, all with solutions or helpful hints; a careful attention to history; implementations of many of the algorithms in his classic step-by-step form."--Frank RuskeyThese four books comprise what easily could be the most important set of information on any serious programmer's bookshelf.

    Laughlin transports us two centuries into the future, when we've ceased to use carbon from the ground--either because humans have banned carbon burning or because fuel has simply run out. Boldly, Laughlin predicts no earth-shattering transformations will have taken place. Six generations from now, there will still be soccer moms, shopping malls, and business trips. Firesides will still be snug and warm.How will we do it? Not by discovering a magic bullet to slay our energy problems, but through a slew of fascinating technologies, drawing on wind, water, and fire. Powering the Future is an objective yet optimistic tour through alternative fuel sources, set in a world where we've burned every last drop of petroleum and every last shovelful of coal.The Predictable: Fossil fuels will run out. The present flow of crude oil out of the ground equals in one day the average flow of the Mississippi River past New Orleans in thirteen minutes. If you add the energy equivalents of gas and coal, it's thirty-six minutes. At the present rate of consumption, we'll be out of fossil fuels in two centuries' time. We always choose the cheapest gas. From the nineteenth-century consolidation of the oil business to the California energy crisis of 2000-2001, the energy business has shown, time and again, how low prices dominate market share. Market forces--not green technology--will be the driver of energy innovation in the next 200 years. The laws of physics remain fixed. Energy will still be conserved, degrade entropically with use, and have to be disposed of as waste heat into outer space. How much energy a fuel can pack away in a given space is fixed by quantum mechanics--and if we want to keep flying jet planes, we will need carbon-based fuels. The Potential: Animal waste. If dried and burned, the world's agricultural manure would supply about one-third as much energy as all the coal we presently consume. Trash. The United States disposes of 88 million tons of carbon in its trash per year. While the incineration of waste trash is not enough to contribute meaningfully to the global demand for energy, it will constrain fuel prices by providing a cheap supply of carbon. Solar energy. The power used to light all the cities around the world is only one-millionth of the total power of sunlight pouring down on earth's daytime side. And the amount of hydropump storage required to store the world's daily electrical surge is equal to only eight times the volume of Lake Mead. PRAISE FOR ROBERT B. LAUGHLIN -Perhaps the most brilliant theoretical physicist since Richard Feynman---George Chapline, Lawrence Livermore National Laboratory -Powerful but controversial.--- Financial Times -[Laughlin's] company ... is inspirational.- --New Scientist

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

     This book is a supplement to What If? and intended to enhance the experience of reading the original book. We recommend purchasing the full version of What If? on Amazon in addition to this book. Introduction What If? Serious Scientific Answers to Absurd Hypothetical Questions presents a wide variety of questions covering a range of dubious potentialities and the results which would ensue should they become reality. The questions are collected from author Randall Munroe’s website, where they are sent in by readers of his blog. Some of the questions are conceptual, for example how much force would be required for Yoda to lift an X-fighter, others are in a more serious vein. All of the answers however are based on research and the application of scientific principles by the author, himself trained in physics and a former roboticist for NASA. Benefits Spend less time reading and more time enjoying your favorite books. Discover important details you may have missed the first time. Review key concepts in an easy-to-understand and efficient manner. Use as a reference or "cheat sheet" to quickly access important information. Pick up where you left off with the original book. Focus only on critical information and eliminate unnecessary details. Buy Now Buy Now: Only $2.99 (Save $3.00 or 50%, Regular Price: $5.99) Money Back Guarantee: If you are not 100% satisfied with your purchase, simply return it to Amazon within 7 days of purchase for a full refund. Go to Your Account -> Manage Your Content and Devices -> Find the Book -> Return for Full Refund. Read Now: Your book will be delivered to your Kindle device or free Kindle software automatically.

    This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

    And then we wonder why software is notorious for being delivered late and full of bugs, while other engineers routinely deliver finished bridges, automobiles, electrical appliances, etc., on time and with only minor defects. This book sets out to redress this imbalance. Members of my advanced development team at Adobe who took the course based on the same material all benefited greatly from the time invested. It may appear as a highly technical text intended only for computer scientists, but it should be required reading for all practicing software engineers." --Martin Newell, Adobe Fellow"The book contains some of the most beautiful code I have ever seen." --Bjarne Stroustrup, Designer of C++"I am happy to see the content of Alex's course, the development and teaching of which I strongly supported as the CTO of Silicon Graphics, now available to all programmers in this elegant little book." --Forest Baskett, General Partner, New Enterprise Associates"Paul's patience and architectural experience helped to organize Alex's mathematical approach into a tightly-structured edifice--an impressive feat!" --Robert W. Taylor, Founder of Xerox PARC CSL and DEC Systems Research Center Elements of Programming provides a different understanding of programming than is presented elsewhere. Its major premise is that practical programming, like other areas of science and engineering, must be based on a solid mathematical foundation. The book shows that algorithms implemented in a real programming language, such as C++, can operate in the most general mathematical setting. For example, the fast exponentiation algorithm is defined to work with any associative operation. Using abstract algorithms leads to efficient, reliable, secure, and economical software.This is not an easy book. Nor is it a compilation of tips and tricks for incremental improvements in your programming skills. The book's value is more fundamental and, ultimately, more critical for insight into programming. To benefit fully, you will need to work through it from beginning to end, reading the code, proving the lemmas, and doing the exercises. When finished, you will see how the application of the deductive method to your programs assures that your system's software components will work together and behave as they must.The book presents a number of algorithms and requirements for types on which they are defined. The code for these descriptions--also available on the Web--is written in a small subset of C++ meant to be accessible to any experienced programmer. This subset is defined in a special language appendix coauthored by Sean Parent and Bjarne Stroustrup.Whether you are a software developer, or any other professional for whom programming is an important activity, or a committed student, you will come to understand what the book's experienced authors have been teaching and demonstrating for years--that mathematics is good for programming, and that theory is good for practice.

    Animals move with astounding grace, speed, and versatility: how do they do it, and what can we learn from them? In How to Walk on Water and Climb up Walls, David Hu takes readers on an accessible, wondrous journey into the world of animal motion. From basement labs at MIT to the rain forests of Panama, Hu shows how animals have adapted and evolved to traverse their environments, taking advantage of physical laws with results that are startling and ingenious. In turn, the latest discoveries about animal mechanics are inspiring scientists to invent robots and devices that move with similar elegance and efficiency.Hu follows scientists as they investigate a multitude of animal movements, from the undulations of sandfish and the way that dogs shake off water in fractions of a second to the seemingly crash-resistant characteristics of insect flight. Not limiting his exploration to individual organisms, Hu describes the ways animals enact swarm intelligence, such as when army ants cooperate and link their bodies to create bridges that span ravines. He also looks at what scientists learn from nature's unexpected feats--such as snakes that fly, mosquitoes that survive rainstorms, and dead fish that swim upstream. As researchers better understand such issues as energy, flexibility, and water repellency in animal movement, they are applying this knowledge to the development of cutting-edge technology.Integrating biology, engineering, physics, and robotics, How to Walk on Water and Climb up Walls demystifies the remarkable mechanics behind animal locomotion.

    In this book, William Cook takes readers on a mathematical excursion, picking up the salesman's trail in the 1800s when Irish mathematician W. R. Hamilton first defined the problem, and venturing to the furthest limits of today's state-of-the-art attempts to solve it. He also explores its many important applications, from genome sequencing and designing computer processors to arranging music and hunting for planets.In Pursuit of the Traveling Salesman travels to the very threshold of our understanding about the nature of complexity, and challenges you yourself to discover the solution to this captivating mathematical problem.

    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.

    Much more commonly, they are linked to sensory deprivation, intoxication, illness, or injury. People with migraines may see shimmering arcs of light or tiny, Lilliputian figures of animals and people. People with failing eyesight, paradoxically, may become immersed in a hallucinatory visual world. Hallucinations can be brought on by a simple fever or even the act of waking or falling asleep, when people have visions ranging from luminous blobs of color to beautifully detailed faces or terrifying ogres. Those who are bereaved may receive comforting “visits” from the departed. In some conditions, hallucinations can lead to religious epiphanies or even the feeling of leaving one’s own body. Humans have always sought such life-changing visions, and for thousands of years have used hallucinogenic compounds to achieve them. As a young doctor in California in the 1960s, Oliver Sacks had both a personal and a professional interest in psychedelics. These, along with his early migraine experiences, launched a lifelong investigation into the varieties of hallucinatory experience. Here, with his usual elegance, curiosity, and compassion, Dr. Sacks weaves together stories of his patients and of his own mind-altering experiences to illuminate what hallucinations tell us about the organization and structure of our brains, how they have influenced every culture’s folklore and art, and why the potential for hallucination is present in us all, a vital part of the human condition.

    It contains sample programs to illustrate practical solutions to the type of real-world problems professional developers encounter.