From the classroom to the courtroom and from financial markets to supermarkets, Mlodinow's intriguing and illuminating look at how randomness, chance, and probability affect our daily lives will intrigue, awe, and inspire.

    Focused on helping students understand and construct proofs and expanding their mathematical maturity, this best-selling text is an accessible introduction to discrete mathematics. Johnsonbaugh's algorithmic approach emphasizes problem-solving techniques. The Seventh Edition reflects user and reviewer feedback on both content and organization.

    Yet, only a few lucky ones get to experience it in their lives. If the beginning of Ravin and Khushi's journey is anything to go by, their story is bound to be anything but conventional. However, if they have been lucky enough to find true love in each other, will they have the strength to deal with the biggest challenge life throws their way?It has been three years since Ravin graduated from engineering college and, slowly but surely, he is beginning to get his ducks in a row. He has a decent job, a stable salary, and has his eyes set on an MBA. Is this a good time to start thinking about 'settling down'? How does one even find love?Read on to see how Ravin goes about finding the answers to his questions in the first part of Ravinder Singh's powerful love story, I Too Had a Love Story.

    After all, he was the heir to the biggest clay works in all of Cornwall, and she was but the daughter of one of their workmen.For just as an unexpected passion began to blossom between Morwen Tremayne and Ben Killigrew, the fates seemed determined to stifle its growth. A long, bitter struggle between owner and workforce brought about Charles Killigrew’s sudden illness, and with it Ben’s summons to the head of the family firm.Morwen and Ben, innocent victims of other men’s antagonisms, find themselves party to a conflict not of their making, that threatens to cut them off not only from their families, but from each other…A touching, heartbreaking tale of a love that conquers all, Killigrew Clay is perfect for fans of Rosie Goodwin, Lesley Pearse and Linda Finlay

    Captain Drew Matthews is sent on a mission behind enemy lines to secure vital information. He is ambushed but is able to escape. Joining a group of American soldiers, led by Corporal Dane Shaw, refugees from the recent battle, they make their way across the desert to the city of Sfax, where Drew is to meet with the mysterious ‘Monsieur Gascoigne’. They stumble across a small group of Arabs and Germans guarding a halftrack filled with treasure looted from murdered Jews and rescue their prisoner, the beautiful Frenchwoman Angelique DuBois. While driving to Sfax, Dane shares his Christian faith with his companions while trying to avoid a group of Arab raiders. Dane relies on God to direct his life while proving himself a wise and outstanding commander with ferocious fighting skills. While Dane and his men fight off the Arab bandits, Drew and Angelique enter Sfax for their meeting only to be met with a number of very unwelcome surprises. Escaping by a series of fortunate circumstances, which leads them to believe that there may be something to Dane’s God, they return to camp only to run into more trouble. Zabronski, one of the American soldiers, filled with lust for the treasure and for Angelique, subverts the other soldiers, kidnaps Drew and Angelique and wounds Dane. Forcing Drew to reveal where the treasure is hidden, they are met by the Germans and Arab bandits who have joined forces and tracked them down. Drew and Angelique are rescued by Dane, and in a wild night ride across the desert, pursued by vengeful Germans and Arabs, they attempt to reach the American lines before dawn. Will they make it, and will Angelique solve the dilemma of which man she loves and which god she will serve?

    Volume 5 of the Sarah Woods Mystery Series A Death in Tuscany (Book 13) The Stares of Strangers (Book 14) The Devil You Know (Book 15)

    This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. Time Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results.The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.-- "Journal of Economics"

    Now, at a moment when mathematicians are finally moving in on a proof, Dartmouth professor Dan Rockmore tells the riveting history of the hunt for a solution.In 1859 German professor Bernhard Riemann postulated a law capable of describing with an amazing degree of accuracy the occurrence of the prime numbers. Rockmore takes us all the way from Euclid to the mysteries of quantum chaos to show how the Riemann hypothesis lies at the very heart of some of the most cutting-edge research going on today in physics and mathematics.

    The book is non-theoretical, explaining concepts intuitively and teaching problem solving through worked examples and step-by-step instructions. This edition places more emphasis on conceptual understanding and understanding results. This edition also features increased emphasis on Excel, MINITAB, and the TI-83 Plus and TI 84-Plus graphing calculators, computing technologies commonly used in such courses.

    It moves beyond a mere study of algorithms without sacrificing the rigor that faculty desire. As in every edition, Winston reinforces the book's successful features and coverage with the most recent developments in the field. The Student Suite CD-ROM, which now accompanies every new copy of the text, contains the latest versions of commercial software for optimization, simulation, and decision analysis.

    But this book will help you to do something that humans have only recently understood how to do: to count to regions that no animal has ever reached. By the end of this book you'll be able to count to infinity... and beyond. On our way to infinity we'll discover how the ancient Babylonians used their bodies to count to 60 (which gave us 60 minutes in the hour), how the number zero was only discovered in the 7th century by Indian mathematicians contemplating the void, why in China going into the red meant your numbers had gone negative and why numbers might be our best language for communicating with alien life.But for millennia, contemplating infinity has sent even the greatest minds into a spin. Then at the end of the nineteenth century mathematicians discovered a way to think about infinity that revealed that it is a number that we can count. Not only that. They found that there are an infinite number of infinities, some bigger than others. Just using the finite neurons in your brain and the finite pages in this book, you'll have your mind blown discovering the secret of how to count to infinity.Do something amazing and learn a new skill thanks to the Little Ways to Live a Big Life books!

    This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

    The content of this book has been used successfully with students whose mathematics background consists of calculus and calculus-based probability. The text gives both precise statements of results, plausibility arguments, and even some proofs, but more importantly intuitive explanations developed and refine through classroom experience with this material are provided. The book includes a self-contained treatment of the probability theory needed for shastic calculus, including Brownian motion and its properties. Advanced topics include foreign exchange models, forward measures, and jump-diffusion processes.This book is being published in two volumes. This second volume develops shastic calculus, martingales, risk-neutral pricing, exotic options and term structure models, all in continuous time.Masters level students and researchers in mathematical finance and financial engineering will find this book useful.Steven E. Shreve is Co-Founder of the Carnegie Mellon MS Program in Computational Finance and winner of the Carnegie Mellon Doherty Prize for sustained contributions to education.

    

    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.