It concentrates on the commercial exploitation of inorganic chemicals date. Every chapter in the book has been revised and updated and follows the IUPAC recommendation that the main groups and the tranisition metals be numbered from 1 to 18.

    KEY TOPICS: Coverage begins with an optional review of key concepts--such as properties of compound semiconductor, quantum mechanics, semiconductor statistics, carrier transport properties, optical processes, and junction theory--then progress gradually through more advanced topics. The Second Edition has been both updated and expanded to include the recent developments in the field.

    Introducing students to the many facets of neural networks, this text provides many case studies to illustrate their real-life, practical applications.

    

    Lehninger Principles of Biochemistry, Fourth Edition brings clarity and coherence to an often unwieldy discipline, while incorporating the field's most important recent developments and applications.

    Highlights of the book include realistic problems and examples from a wide range of application areas. New to this edition are: much sharper pedagogy; an increase in the number of examples; more thorough development of the concepts; a greater range of homework problems; a greater number and variety of worked out examples; expanded coverage of dynamics modelling and Laplace transform topics; and integration of MATLAB, including many examples that are formatted in MATLAB.

    The manner in which computers are now able to mimic human thinking is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest. In the age of machine learning, computers do not strictly need to receive an ‘input command’ to perform a task, but rather ‘input data’. From the input of data they are able to form their own decisions and take actions virtually as a human would. But as a machine, can consider many more scenarios and execute calculations to solve complex problems. This is the element that excites companies and budding machine learning engineers the most. The ability to solve complex problems never before attempted. This is also perhaps one reason why you are looking at purchasing this book, to gain a beginner's introduction to machine learning. This book provides a plain English introduction to the following topics: - Artificial Intelligence - Big Data - Downloading Free Datasets - Regression - Support Vector Machine Algorithms - Deep Learning/Neural Networks - Data Reduction - Clustering - Association Analysis - Decision Trees - Recommenders - Machine Learning Careers This book has recently been updated following feedback from readers. Version II now includes: - New Chapter: Decision Trees - Cleanup of minor errors

    New topics include managing and maintaining cellular devices, including tablets; configuring operating systems, including Windows 8, Android, and iOS; and enhanced, mobile-centered security and troubleshooting procedures. The All-in-One Exam Guide enables you to take the test with complete confidence. It also serves as a practical reference for IT support and technical personnel. Bonus electronic content includes: Practice exams with hundreds of accurate questions More than an hour of video training featuring Mike Meyers Performance-based simulations that prepare you for the performance-based questions on the exam A collection of Mike's favorite free PC tools Key Features include: Written with the “in the trenches” voice and clarity Mike Meyers is known for Features pre-assessment tests, exam tips, and “Try This!” sections to reinforce difficult topics Includes a coupon for 10% off of the exam fee, a $37 value

    This book/CD-ROM package has now been updated to cover the latest version of the CCNA exam.

    Sooner or later, nearly all climbers get injured and it will be injuries that ultimately dictate how far you get in climbing, if you let them. Unfortunately, the data shows it takes over a decade just to get small proportions of medical research adopted in regular practice. Sourcing reliable and up to date advice on preventing and treating finger, elbow, shoulder and other climbing injuries is challenging to say the least. You need to be the expert, because there are so many strands of knowledge and practice to pull together to stay healthy as a climber, and no single source of advice to cover all of these. The book draws together both the cutting edge of peer reviewed sports medicine research, and the subtle concepts of changing your climbing habits and routine to prevent and successfully recover from injuries. It is a handbook on how to take care of yourself as a lifelong climbing athlete. By spanning the fields of climbing coaching, physiotherapy, sports medicine and behavioural science, it goes beyond the general advice on treating symptoms offered by sports medicine textbooks and into much more detail on technique and habits specific to climbing than the existing climbing literature base. You will learn how your current climbing habits are already causing your future injuries and what you can do to change that. If you are already injured, it will prevent you from prolonging your injury with the wrong climbing habits and rehabilitation choices. You will learn how the ingredients of prevention and good recovery come from wildly different sources and how you have been using only a fraction of them. Fully referenced throughout, the practical advice for diagnosis, rehabilitation and prevention of climbing injuries is drawn from up to date peer reviewed sports medicine research.

    With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora

    It covers the basics of computer programming, including variables and values, functions, conditionals and control flow, program development and debugging. Later chapters cover basic algorithms and data structures.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    Written by A330 Captain, Bill Palmer, this book opens to understanding the actions of the crew, how they failed to understand and control the problem, and how the airplane works and the part it played. All in easy to understand terms.Addressed are the many contributing aspects of weather, human factors, and airplane system operation and design that the crew could not recover from. How each contributed is covered in detail along with what has been done, and needs to be done in the future to prevent this from happening again.Also see the book's companion website: UnderstandingAF447.com for supplemental materials referred to in the book or to contact the author.

    For anyone who has ever wondered how computers solve problems, an engagingly written guide for nonexperts to the basics of computer algorithms.