It discusses neuroanatomy, cell and molecular mechanisms and signaling through a cognitive approach to behaviour. It features an expanded treatment of the nervous system, neurological and psychiatric diseases and perception.

    Industry experts Jacqueline Nolis and Emily Robinson lay out the soft skills you’ll need alongside your technical know-how in order to succeed in the field. Following their clear and simple instructions you’ll craft a resume that hiring managers will love, learn how to ace your interview, and ensure you hit the ground running in your first months at your new job. Once you’ve gotten your foot in the door, learn to thrive as a data scientist by handling high expectations, dealing with stakeholders, and managing failures. Finally, you’ll look towards the future and learn about how to join the broader data science community, leaving a job gracefully, and plotting your career path. With this book by your side you’ll have everything you need to ensure a rewarding and productive role in data science.

    "His book is very compatible with XP. It is not about drawing pictures of a domain; it is about how you think of it, the language you use to talk about it, and how you organize your software to reflect your improving understanding of it. Eric thinks that learning about your problem domain is as likely to happen at the end of your project as at the beginning, and so refactoring is a big part of his technique. "The book is a fun read. Eric has lots of interesting stories, and he has a way with words. I see this book as essential reading for software developers--it is a future classic." --Ralph Johnson, author of Design Patterns "If you don't think you are getting value from your investment in object-oriented programming, this book will tell you what you've forgotten to do. "Eric Evans convincingly argues for the importance of domain modeling as the central focus of development and provides a solid framework and set of techniques for accomplishing it. This is timeless wisdom, and will hold up long after the methodologies du jour have gone out of fashion." --Dave Collins, author of Designing Object-Oriented User Interfaces "Eric weaves real-world experience modeling--and building--business applications into a practical, useful book. Written from the perspective of a trusted practitioner, Eric's descriptions of ubiquitous language, the benefits of sharing models with users, object life-cycle management, logical and physical application structuring, and the process and results of deep refactoring are major contributions to our field." --Luke Hohmann, author of Beyond Software Architecture "This book belongs on the shelf of every thoughtful software developer." --Kent Beck "What Eric has managed to capture is a part of the design process that experienced object designers have always used, but that we have been singularly unsuccessful as a group in conveying to the rest of the industry. We've given away bits and pieces of this knowledge...but we've never organized and systematized the principles of building domain logic. This book is important." --Kyle Brown, author of Enterprise Java(TM) Programming with IBM(R) WebSphere(R) The software development community widely acknowledges that domain modeling is central to software design. Through domain models, software developers are able to express rich functionality and translate it into a software implementation that truly serves the needs of its users. But despite its obvious importance, there are few practical resources that explain how to incorporate effective domain modeling into the software development process. Domain-Driven Design fills that need. This is not a book about specific technologies. It offers readers a systematic approach to domain-driven design, presenting an extensive set of design best practices, experience-based techniques, and fundamental principles that facilitate the development of software projects facing complex domains. Intertwining design and development practice, this book incorporates numerous examples based on actual projects to illustrate the application of domain-driven design to real-world software development. Readers learn how to use a domain model to make a complex development effort more focused and dynamic. A core of best practices and standard patterns provides a common language for the development team. A shift in emphasis--refactoring not just the code but the model underlying the code--in combination with the frequent iterations of Agile development leads to deeper insight into domains and enhanced communication between domain expert and programmer. Domain-Driven Design then builds on this foundation, and addresses modeling and design for complex systems and larger organizations.Specific topics covered include:Getting all team members to speak the same language Connecting model and implementation more deeply Sharpening key distinctions in a model Managing the lifecycle of a domain object Writing domain code that is safe to combine in elaborate ways Making complex code obvious and predictable Formulating a domain vision statement Distilling the core of a complex domain Digging out implicit concepts needed in the model Applying analysis patterns Relating design patterns to the model Maintaining model integrity in a large system Dealing with coexisting models on the same project Organizing systems with large-scale structures Recognizing and responding to modeling breakthroughs With this book in hand, object-oriented developers, system analysts, and designers will have the guidance they need to organize and focus their work, create rich and useful domain models, and leverage those models into quality, long-lasting software implementations.

    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.

    Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud-computing technologies. This collection of essays expands on the vision of pioneering computer scientist Jim Gray for a new, fourth paradigm of discovery based on data-intensive science and offers insights into how it can be fully realized.

    This book teaches you how to conduct examinations by explaining what digital forensics is, the methodologies used, key technical concepts and the tools needed to perform examinations. Details on digital forensics for computers, networks, cell phones, GPS, the cloud, and Internet are discussed. Readers will also learn how to collect evidence, document the scene, and recover deleted data. This is the only resource your students need to get a jump-start into digital forensics investigations.This book is organized into 11 chapters. After an introduction to the basics of digital forensics, the book proceeds with a discussion of key technical concepts. Succeeding chapters cover labs and tools; collecting evidence; Windows system artifacts; anti-forensics; Internet and email; network forensics; and mobile device forensics. The book concludes by outlining challenges and concerns associated with digital forensics. PowerPoint lecture slides are also available.This book will be a valuable resource for entry-level digital forensics professionals as well as those in complimentary fields including law enforcement, legal, and general information security.

    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.

    Techniques covered include state- and goal-based behavior, inter-agent communication, individual and group steering behaviors, team AI, graph theory, search, path planning and optimization, triggers, scripting, scripted finite state machines, perceptual modeling, goal evaluation, goal arbitration, and fuzzy logic.

    Publilius Syrus, Moral Sayings We've been very fortunate to receive fantastic feedback from our readers during the last four years, since the first edition of How to Solve It: Modern Heuristics was published in 1999. It's heartening to know that so many people appreciated the book and, even more importantly, were using the book to help them solve their problems. One professor, who published a review of the book, said that his students had given the best course reviews he'd seen in 15 years when using our text. There can be hardly any better praise, except to add that one of the book reviews published in a SIAM journal received the best review award as well. We greatly appreciate your kind words and personal comments that you sent, including the few cases where you found some typographical or other errors. Thank you all for this wonderful support.

    "Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, the author uses the flexible Python programming language to show how to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

    Divided into two parts, the first part of the book introduces the programming language Prolog, while the second part teaches Artificial Intelligence using Prolog as a tool for the implementation of AI techniques. Prolog has its roots in logic, however the main aim of this book is to teach Prolog as a practical programming tool. This text therefore concentrates on the art of using the basic mechanisms of Prolog to solve interesting problems. The third edition has been fully revised and extended to provide an even greater range of applications, which further enhance its value as a self-contained guide to Prolog, AI or AI Programming for students and professional programmers alike.

    Unlike other introductory books, it focuses on the program design process. This approach fosters a variety of skills--critical reading, analytical thinking, creative synthesis, and attention to detail--that are important for everyone, not just future computer programmers. The book exposes readers to two fundamentally new ideas. First, it presents program design guidelines that show the reader how to analyze a problem statement; how to formulate concise goals; how to make up examples; how to develop an outline of the solution, based on the analysis; how to finish the program; and how to test. Each step produces a well-defined intermediate product. Second, the book comes with a novel programming environment, the first one explicitly designed for beginners. The environment grows with the readers as they master the material in the book until it supports a full-fledged language for the whole spectrum of programming tasks.All the book's support materials are available for free on the Web. The Web site includes the environment, teacher guides, exercises for all levels, solutions, and additional projects.A second edition is now available.

    The book also provides a balanced presentation of time-varying and static fields, preparingstudents for employment in today's industrial and manufacturing sectors. Streamlined to facilitate student understanding, this edition features worked examples in every chapter that explain how to use the theory presented in the text to solve different kinds of problems. Numerical methods, including MATLAB and vector analysis, are also included to help students analyzesituations that they are likely to encounter in industry practice. Elements of Electromagnetics, Fifth Edition, is designed for introductory undergraduate courses in electromagnetics.

    It provides a simple, thorough survey of elementary topics, starting with set theory and advancing to metric and topological spaces, connectedness, and compactness. 1975 edition.

    Rather than a "how to" for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques.You have been predicted — by companies, governments, law enforcement, hospitals, and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die.Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales.How? Prediction is powered by the world's most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future — lifting a bit of the fog off our hazy view of tomorrow — means pay dirt.In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: -What type of mortgage risk Chase Bank predicted before the recession. -Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves. -Why early retirement decreases life expectancy and vegetarians miss fewer flights. -Five reasons why organizations predict death, including one health insurance company. -How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual. -How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! -How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. -How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free. -What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia. A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward — but that can be predicted in advance?Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.