Restructured into six parts, this new edition covers a wide spectrum of software processes from initial requirements solicitation through design and development.

    Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code f

    Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

    "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.

    Because of this balance, the Seventh Edition of Microbiology is appropriate for microbiology majors and mixed majors courses. The new authors have focused on readability, artwork, and the integration of several key themes (including evolution, ecology and diversity) throughout the text, making an already superior text even better.

    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.

    This series has three preparation courses, Academic Reading, Academic Writing, and Listening and Speaking. The courses develop language, skills and test techniques to help students achieve a higher IELTS score.

    Unfortunately, most programmers frequently have a limited understanding of these essential mathematics and physics concepts. MATHEMATICS AND PHYSICS FOR PROGRAMMERS, THIRD EDITION provides a simple but thorough grounding in the mathematics and physics topics that programmers require to write algorithms and programs using a non-language-specific approach. Applications and examples from game programming are included throughout, and exercises follow each chapter for additional practice. The book's companion website provides sample code illustrating the mathematical and physics topics discussed in the book.

    It is also about Python. However, there is much more. The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum. In addition, a beginner needs to be given the opportunity to be successful and gain confidence. This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum. Even though the second course is considered more advanced than the first course, this book assumes you are beginners at this level. You may still be struggling with some of the basic ideas and skills from a first computer science course and yet be ready to further explore the discipline and continue to practice problem solving. We cover abstract data types and data structures, writing algorithms, and solving problems. We look at a number of data structures and solve classic problems that arise. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.

    This book also takes into account the period of emerging adulthood (ages 18-25), an area sometimes neglected but of particular interest to many students who see themselves reflected in the research. Looking for additional resources to help you understand the material and succeed in this course? MyDevelopmentLab contains study tools such as flashcards, self tests, videos, as well as MyVirtulTeen which allows you to raise your own virtual teenager, focusing on the ages 10 through 18. MyDevelpmentLab is available at www.mydevelopmentlab.com.

    With extensive treatment of the heart of epidemiology-from study designs to descriptive epidemiology to quantitative measures-this reader-friendly text is accessible and interesting to a wide range of beginning students in all health-related disciplines. A unique focus is given to real-world applications of epidemiology and the development of skills that students can apply in subsequent course work and in the field. The text is also accompanied by a complete package of instructor and student resources available through a companion Web site.

    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.

    Loosely based on neuron behavior inside of human brains, these systems are rapidly catching up with the intelligence of their human creators, defeating the world champion Go player, achieving superhuman performance on video games, driving cars, translating languages, and sometimes even helping law enforcement fight crime. Deep Learning is a revolution that is changing every industry across the globe.Grokking Deep Learning is the perfect place to begin your deep learning journey. Rather than just learn the “black box” API of some library or framework, you will actually understand how to build these algorithms completely from scratch. You will understand how Deep Learning is able to learn at levels greater than humans. You will be able to understand the “brain” behind state-of-the-art Artificial Intelligence. Furthermore, unlike other courses that assume advanced knowledge of Calculus and leverage complex mathematical notation, if you’re a Python hacker who passed high-school algebra, you’re ready to go. And at the end, you’ll even build an A.I. that will learn to defeat you in a classic Atari game.

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    As the field of environmental science continues to evolve, this highly readable guide presents a full spectrum of views and information for students to evaluate issues and make informed decisions. An extensive resource package integrates text and digital media in an easy-to-use format designed to assist instructors in classroom preparation.