Have you ever opened a Kindle book to find that the font started out way too small or way too large? Have you tried to change to a different font while reading and discovered you couldn't? Have you jumped to a new chapter in a Kindle book and seen that the chapter heading lost its formatting? Has a Kindle completely ignored formatting you knew was in the book? According to Amazon, the simplest way to publish your Kindle book is to upload an HTML file you've saved from Microsoft Word or another app. By itself, that method can bring you maybe 80% of the way to a well-formatted, trouble-free ebook. But what about the other 20%? In this follow-up to his bestselling -From Word to Kindle, - Aaron Shepard takes your saved HTML as a starting point and tells how to quickly tweak and tune it to avoid common problems. Assuming no knowledge of HTML, he introduces the basics of the language, then reveals how to use find-and-replace and macros to touch up an entire book in seconds! If you're serious about Kindle publishing and you're technically inclined -- but not a full-fledged geek -- Aaron provides the tips you need to bring your Kindle book to the next level, making it something truly to be proud of. ///////////////////////////////////////////////// Aaron Shepard is a foremost proponent of the new business of profitable self publishing, which he has practiced and helped develop since 1998. He is the author of -Aiming at Amazon, - -POD for Profit, - -Perfect Pages, - and Amazon's #1 and #2 bestselling paid books on Kindle formatting, -From Word to Kindle- and -Pictures on Kindle.- ///////////////////////////////////////////////// CONTENTS Getting Started 1 WORKING WITH HTML HTML and Kindle HTML Export HTML Editing HTML Processing HTML Basics HTML Checking HTML Cleanup HTML Testing 2 HTML FIXES Fixes for Fonts Fixes for Paragraphs Fixes for Headings Fixes for Line Breaking Fixes for Pictures Fixes for Navigation ///////////////////////////////////////////////// SAMPLE Here are some of the things you can accomplish through changes in HTML. * Adjust bookmarks so headings retain proper formatting when jumped to. * Remove settings that stop the user from choosing their own. * Keep fonts from appearing much too small or much too large when the book is opened. * Make sure indents and other spacing stays relative to larger and smaller font sizes. * Avoid line breaks that leave short words dangling at the ends of lines or paragraphs. * Make up for features lost in translation from your word processor, like nonbreaking hyphens. * Stop -ghost hyphens- from appearing in the middle of words. * Keep pages of text from disappearing for some users. * Prevent the Kindle from applying its own defaults in place of your settings.

    This supplement will cater to the requirements of students by covering all important topics of Laplace transformation, Matrices, Numerical Methods. Further enhanced is its usability by inclusion of chapter end questions in sync with student needs. Table of contents: 1. Basic Concepts 2. An Introduction to Modeling and Qualitative Methods 3. Classification of First-Order Differential Equations 4. Separable First-Order Differential Equations 5. Exact First-order Differential Equations 6. Linear First-Order Differential Equations 7. Applications of First-Order Differential Equations 8. Linear Differential Equations: Theory of Solutions 9. Second-Order Linear Homogeneous Differential Equations with Constant Coefficients 10. nth-Order Linear Homogeneous Differential Equations with Constant Coefficients 11. The Method of Undetermined Coefficients 12. Variation of Parameters 13. Initial-Value Problems for Linear Differential Equations 14. Applications of Second-Order Linear Differential Equations 15. Matrices 16. eAt 17. Reduction of Linear Differential Equations to a System of First-Order Equations 18. Existence and Uniqueness of Solutions 19. Graphical and Numerical Methods for Solving First-Order Differential Equations 20. Further Numerical Methods for Solving First-Order Differential Equations 21. Numerical Methods for Solving Second-Order Differential Equations Via Systems 22. The Laplace Transform 23. Inverse Laplace Transforms 24. Convolutions and the Unit Step Function 25. Solutions of Linear Differential Equations with Constant Coefficients by Laplace Transforms 26. Solutions of Linear?Systems by Laplace Transforms 27. Solutions of Linear Differential Equations with Constant Coefficients by Matrix Methods 28. Power Series Solutions of Linear Differential Equations with Variable Coefficients 29. Special Functions 30. Series Solutions N

    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.

    With Step By Step, you set the pace-building and practicing the skills you need, just when you need them! Topics include building a project plan and fine-tuning the details; scheduling tasks, assigning resources, and managing dependencies; monitoring progress and costs; keeping projects on track; communicating project data through Gantt charts and other views.

    In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

    Other new features include a chapter on simulation, a section on Gibbs sampling, what you should know boxes at the end of each chapter, and remarks to highlight difficult concepts.

    Hand argues that extraordinarily rare events are anything but. In fact, they’re commonplace. Not only that, we should all expect to experience a miracle roughly once every month.     But Hand is no believer in superstitions, prophecies, or the paranormal. His definition of “miracle” is thoroughly rational. No mystical or supernatural explanation is necessary to understand why someone is lucky enough to win the lottery twice, or is destined to be hit by lightning three times and still survive. All we need, Hand argues, is a firm grounding in a powerful set of laws: the laws of inevitability, of truly large numbers, of selection, of the probability lever, and of near enough.     Together, these constitute Hand’s groundbreaking Improbability Principle. And together, they explain why we should not be so surprised to bump into a friend in a foreign country, or to come across the same unfamiliar word four times in one day. Hand wrestles with seemingly less explicable questions as well: what the Bible and Shakespeare have in common, why financial crashes are par for the course, and why lightning does strike the same place (and the same person) twice. Along the way, he teaches us how to use the Improbability Principle in our own lives—including how to cash in at a casino and how to recognize when a medicine is truly effective.     An irresistible adventure into the laws behind “chance” moments and a trusty guide for understanding the world and universe we live in, The Improbability Principle will transform how you think about serendipity and luck, whether it’s in the world of business and finance or you’re merely sitting in your backyard, tossing a ball into the air and wondering where it will land.

    By following the recommendations of the Association of Computing Machinery's Java Task Force, this first edition text adopts a modern objects-first approach that introduces readers to useful hierarchies from the very beginning.KEY TOPICS: Introduction; Programming by Example; Expressions; Statement Forms; Methods; Objects and Classes; Objects and Memory; Strings and Characters; Object-Oriented Graphics; Event-Driven Programs; Arrays and ArrayLists; Searching and Sorting; Collection Classes; Looking Ahead.MARKET: A modern objects-first approach to the Java programming language that introduces readers to useful class hierarchies from the very beginning.

    This title is aimed at courses in applied econometrics, political methodology, and sociological methods or a one-year graduate course in econometrics for social scientists.

    They'll also find the related analytic geometry much easier. The clear review of algebra and geometry in this edition will make calculus easier for students who wish to strengthen their knowledge in these areas. Updated to meet the emphasis in current courses, this new edition of a popular guide--more than 104,000 copies were bought of the prior edition--includes problems and examples using graphing calculators..

    The book's approach interweaves traditional topics with data analysis and reflects the use of the computer with close ties to the practice of statistics. The author stresses analysis of data, examines real problems with real data, and motivates the theory. The book's descriptive statistics, graphical displays, and realistic applications stand in strong contrast to traditional texts which are set in abstract settings.

    Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

    The new edition provides invitations - not requirements - to use technology, as well as new conceptual problems, and new projects that focus on writing and working in teams.

    Subsequent sections deal with integrating factors; dilution and accretion problems; linearization of first order systems; Laplace Transforms; Newton's Interpolation Formulas, more.

    I also hope that it will serve as a useful reference too for the many workers engaged in one type of solid state research activity or another, who may be without formal training in the subject.