Unfortunately, the subject has gained a notorious reputation for difficulty, with forbidding looking mathematics and a peculiar diagrammatic language described in an array of unforgiving, weighty textbooks aimed firmly at aspiring professionals. However, quantum field theory is too important, too beautiful, and too engaging to be restricted to the professionals. This book on quantum field theory is designed to be different. It is written by experimental physicists and aims to provide the interested amateur with a bridge from undergraduate physics to quantum field theory. The imagined reader is a gifted amateur, possessing a curious and adaptable mind, looking to be told an entertaining and intellectually stimulating story, but who will not feel patronised if a few mathematical niceties are spelled out in detail. Using numerous worked examples, diagrams, and careful physically motivated explanations, this book will smooth the path towards understanding the radically different and revolutionary view of the physical world that quantum field theory provides, and which all physicists should have the opportunity to experience.To request a copy of the Solutions Manual, visit http: //global.oup.com/uk/academic/physics/ad....

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    Employing a methodology that is primarily learning by example and “thinking with our eyes,” this manual features graphs and practical analytical techniques that can be applied to a broad range of data analysis tools—including the most commonly used Microsoft Excel. This approach is particularly valuable to those who need to make sense of quantitative business data by discerning meaningful patterns, trends, relationships, and exceptions that reveal business performance, potential problems and opportunities, and hints about the future. It provides practical skills that are useful to managers at all levels and to those interested in keeping a keen eye on their business.

    It reveals the attraction that has drawn leading mathematicians and amateurs alike to number theory over the course of history.

    You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples--ready for immediate application to your next graph or presentation.Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data--Storytelling with Data will give you the skills and power to tell it!

     Crochet for beginners will include: Step-by-step guide: Clear & precise instructions so you will not feel lost in between steps Clear picture illustrations: High quality illustrations with each step so you can understand the instructions better 27 Beginner Crochet Hacks: Top tips, tricks & hacks every beginner crocheter needs to know to avoid costly mistakes & learn faster 25 Beginner crochet patterns: Beginner crochet patterns with complete instructions & pictures are included within the book so you can start to create your own beautiful crochet patterns immediately Crochet for beginners 2.0 revised edition! 25 beginner patterns are included within the book without having to click any external links! Clear pictures & instructions are provided as well. Crocheting does not need to be hard if you follow the right guide. Grab this best-selling book today & you will be crocheting beautiful patterns in no time! Simply download your copy above now to get started!

    The exposition is simple and leads to the most complete direct means of solving problems in mechanics. The final sections on adiabatic invariants have been revised and augmented. In addition a short biography of L D Landau has been inserted.

    This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

    It provides dozens of new examples from actual research studies are used to provide illustrations of concepts and methods. Key terms are now called out and defined in boxes at the bottom of the pages where they appear, for easier study and review. In chapter 1, there are now separate descriptions and examples of the steps in the research process for quantitative and qualitative approaches, to underscore some of the fundamental differences. Chapter 2 has new discussions of participatory action research, instrumental and reflexive knowledge, the various audiences for social research findings, and researcher autonomy when research is commissioned. The discussion of social theories in Chapter 3 now covers levels of abstraction, and relationships among concepts

    With an accessible and lively writing style, it introduces modern techniques to what can often be a formal and intimidating subject. Readers are led from the physics of flat spacetime (special relativity), through the intricacies of differential geometry and Einstein's equations, and on to exciting applications such as black holes, gravitational radiation, and cosmology.

    Proving once again that you don't need loads of time to cook great food, Joe’s 30-Minute Meals is packed with everyday healthy dishes that you'll want to make, time and time again.Most chapters are simply organised by main ingredient, making it easy to find a recipe for every occasion. Every recipe is labelled as either reduced-carb or carb-refuel, which makes this book a brilliant addition for those already familiar with Joe's popular Lean in 15 eating structure (carbs following workouts or on active days), but the meals are also enjoyable and tasty for all, whether you are following Joe's plan or not.If you're looking for a crowd-pleasing lunch like the Bang Bang Chicken Stir-Fry or something tasty to chuck in the oven at the end of a long day such as Joe’s Sausage and Mushroom Pie, Joe's 30-Minute Meals makes it easy to make a fuss-free, tasty plate of food, every single day.

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    This book provides an introduction to the field of solid state physics for undergraduate students in physics, chemistry, engineering, and materials science.

    Stephen Montgomery presents a fresh new look at the four temperaments, the four ancient "people patterns" that are the key to personality types. In this updated and expanded 2nd edition, Dr. Montgomery cites over 250 characters from well-known movies and TV shows The Wizard of Oz, Sex and the City, Harry Potter, Star Trek, Star Wars, The Fantastic Four, The Incredibles, The Lord of the Rings, Ghost Busters, and many more to help bring the temperaments alive for a modern audience.People Patterns features an easy-to-score personality quiz, and easy-to-read chapters on dating & mating, parents & children, and talent & career (with over 750 job suggestions grouped according to type). The book begins with a brief history of the four temperaments (tracing the idea back to Hippocrates), and it ends with port

    Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. "Introduction to Machine Learning" is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.