Search results for ""Authors Co""

As incredible as it may sound, Knack Planning Your Wedding makes wedding decisions truly stress free. The authors, co-founders of the popular wedding planning website JunebugWeddings.com, guide readers thorugh every single detail of planning a perfect, one-of-a-kind wedding.

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Dr Helen McGrath is an Adjunct Professor in School of Education, Deakin University and the School of Education, RMIT University. She is also a counselling psychologist in private practice, a member of the National Centre Against Bullying and was the lead developer of the eSmart Cybersafety Framework and Investigator on the Australian Government's Scoping Study on Approaches to Student Wellbeing. Toni Noble is an Adjunct Professor for Positive Psychology and Education at the Australian Catholic University (ACU) and lead investigator on Australian Government's Scoping Study on Approaches to Student Wellbeing. Both are authors of numerous academic papers, chapters and books for teachers, health professionals and the general community and were co-developers of the National Safe Schools Framework (2011) and the Safe Schools Hub (now Student Wellbeing Hub. They are authors/co-authors of the following teacher resource books: Friendly Kids, Frie

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A collection of sizzling summer-themed crime and mystery stories by thirteen of the most popular and exciting children's book authors! Co-edited by Serena Patel, the award-winning author of the Anisha: Accidental Detective series and by Robin Stevens, author of the bestselling Murder Most Unladylike series.This gorgeous summery collection brings back together thirteen bestselling, award-winning and exciting authors: Abiola Bello, Annabelle Sami, Benjamin Dean, E.L. Norry, Elle McNicoll, Dominique Valente, Maisie Chan, Nizrana Farook, Patrice Lawrence, Robin Stevens, Roopa Farooki, Serena Patel and Sharna Jackson. With stunning illustrations by Harry Woodgate.Grab your sunnies and your magnifying glass, and join the Murder Club for more sensational short stories as they lead you on a journey of foul play and murderously magnificent mysteries! Including sun-thieves, fantastical creatures of the deep, airport mayhem, weddings gone wrong, tropical resort travesties, and a very sticky situa

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The AIB Fellows Group includes top researchers, educators, and administrators in the IB field. Most of its 60 members have contributed to this edited volume as authors, co-authors and reviewers, including such noteworthy scholars as John Dunning, Alan Rugman and Yair Aharoni, among many others.Its chapters examine aspects of the growth of the field, evaluate our present state of knowledge and outline future lines of research. They cover the growth of several functional areas (marketing, advertising, finance, etc.), review problems of methodological rigor in IB research, trace the history and evolution of IB studies and their likely future trajectories, raise ethical and moral issues about IB practices and evaluate the impact of major theories on IB studies. A couple of chapters cover the history of international business and of the AIB Fellows Group. Altogether, this book provides a benchmark of where IB knowledge stands today and will grow in coming years.

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Neurological disorders in children are common: families want to know what is wrong, why it happened, and whether it will happen again. Management and treatment depend on establishing the diagnosis, which usually requires investigations, but the number of possible neurological investigations is now very large indeed, and uncritical investigations may be seriously misleading and often costly. This book, based on the authors’ vast combined personal experience, gives practical guidance on how to target any specific condition with the minimum of tests. Part 1 is a brief distillation of advice on clinical history and examination, introducing the problems that beset those who deal with neurological disorders in childhood. Part 2 outlines the investigations available to solve these diagnostic problems. Part 3 takes a problem-oriented approach to the most appropriate investigations in the various clinical scenarios that may present to the practitioner: the starting point is the patient’s presentation, not the diagnosis. Throughout, vignettes of real cases help to illustrate the use of the tests in different clinical situations. The authors co-wrote the hugely popular Handbook of Neurological Investigations (“the Blue Book”) 20 years ago. This new book, while closely following the highly successful approach of the earlier title, is thoroughly up to date, with fresh material, new case vignettes, and additional investigations covered.

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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

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Help your students to think critically and creatively through team-based problem solving instead of focusing on testing and outcomes. Professionals throughout the education system are recognizing that standardized testing is holding students back. Schools tend to view children as outcomes rather than as individuals who require guidance on thinking critically and creatively. Awesome Math focuses on team-based problem solving to teach discrete mathematics, a subject essential for success in the STEM careers of the future. Built on the increasingly popular growth mindset, this timely book emphasizes a problem-solving approach for developing the skills necessary to think critically, creatively, and collaboratively. In its current form, math education is a series of exercises: straightforward problems with easily-obtained answers. Problem solving, however, involves multiple creative approaches to solving meaningful and interesting problems. The authors, co-founders of the multi-layered educational organization AwesomeMath, have developed an innovative approach to teaching mathematics that will enable educators to: Move their students beyond the calculus trap to study the areas of mathematics most of them will need in the modern world Show students how problem solving will help them achieve their educational and career goals and form lifelong communities of support and collaboration Encourage and reinforce curiosity, critical thinking, and creativity in their students Get students into the growth mindset, coach math teams, and make math fun again Create lesson plans built on problem based learning and identify and develop educational resources in their schools Awesome Math: Teaching Mathematics with Problem Based Learning is a must-have resource for general education teachers and math specialists in grades 6 to 12, and resource specialists, special education teachers, elementary educators, and other primary education professionals.

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Power Flow Control Solutions for a Modern Grid using SMART Power Flow Controllers Provides students and practicing engineers with the foundation required to perform studies of power system networks and mitigate unique power flow problems Power Flow Control Solutions for a Modern Grid using SMART Power Flow Controllers is a clear and accessible introduction to power flow control in complex transmission systems. Starting with basic electrical engineering concepts and theory, the authors provide step-by-step explanations of the modeling techniques of various power flow controllers (PFCs), such as the voltage regulating transformer (VRT), the phase angle regulator (PAR), and the unified power flow controller (UPFC). The textbook covers the most up-to-date advancements in the Sen transformer (ST), including various forms of two-core designs and hybrid architectures for a wide variety of applications. Beginning with an overview of the origin and development of modern power flow controllers, the authors explain each topic in straightforward engineering terms—corroborating theory with relevant mathematics. Throughout the text, easy-to-understand chapters present characteristic equations of various power flow controllers, explain modeling in the Electromagnetic Transients Program (EMTP), compare transformer-based and mechanically-switched PFCs, discuss grid congestion and power flow limitations, and more. This comprehensive textbook: Describes why effective Power Flow Controllers should be viewed as impedance regulators Provides computer simulation codes of the various power flow controllers in the EMTP programming language Contains numerous worked examples and data cases to clarify complex issues Includes results from the simulation study of an actual network Features models based on the real-world experiences the authors, co-inventors of first-generation FACTS controllers Written by two acknowledged leaders in the field, Power Flow Control Solutions for a Modern Grid using SMART Power Flow Controllers is an ideal textbook for graduate students in electrical engineering, and a must-read for power engineering practitioners, regulators, and researchers.

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