Reprinted in hardcover in 1997 to celebrate the 90th anniversary of Neiman Marcus, it is now available for the first time in paperback. Mr. Marcus spent most of his life not only in helping to create a retailing enterprise renowned throughout the world as the epitome of quality, but also in setting high standards for the level of taste of all who desire “the better things in life.” In doing so he has played a key role in making Dallas itself a success. “Mr. Stanley,” as he was affectionately called by all his Neiman Marcus friends and associates, made The Store a legendary success. Although he retired from active involvement in Neiman Marcus in 1977, the influences of the philosophies of business he developed remained an important part of the training of Neiman Marcus personnel. Those basic principles—best exemplified by his belief in his father’s business philosophy—are the reasons Neiman Marcus is today recognized as the taste leader of American retailing.Minding the Store is a warm portrait of a man and an exuberant celebration of the store that has become the best-known landmark in Texas since the Alamo.

    Harrowing and heroic, the events of D-Day were recorded in the personal writings of those who were there. Here, Francis Crosby has compiled a comprehensive collection of previously unpublished letters, diaries, photographs, and reminiscences that tell the story of D-Day as it has never been told before.With the use of new international archives, Crosby has culled vivid and detailed eye-witness accounts from each beach, as well as perspectives from land, sea, and air. This fascinating collection includes entries from American, British, and Canadian troops, the Merchant Navy and the Royal Air Force, and newly available German materials. Also included are contemporary and retrospective reactions of women "in the know" and those whom knew from "unofficial sources" of the immediate imminence of the assault.

    

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    Even though we all have heard that the odds of being struck by lightning are greater than the odds of perishing in a plane crash, it still doesn't feel that way. Airplane crashes might be rare, but they do happen, and they’re usually fatal. David Soucie insists that most of these deaths could be prevented.He’s worked as a pilot, a mechanic, an FAA inspector, and an aviation executive. He’s seen death up close and personal—deaths of colleagues and friends that might have been pre-vented if he had approved certain safety measures in the aircrafts they were handling. His years of experience have led Dave to become an impassioned consultant on the topic of air-line safety. This includes not only advising the Obama administration, but also taking a leading role in the congressionally funded NextGen interdepartmental initiative in regards to both the department of transportation and the departments of defense, homeland security, FBI, CIA, and others. Find out the truth about airplane safety and discover what the future holds for air travel.

    To understand it fully, we must look beneath the surface. The positive side is that technology is making machines smarter. However, the deeper view explained in this book shows that AI is also making a growing number of people cognitively and psychologically dependent on digital networks. Whether you are a social media fanatic, a diehard AI aficionado, or a paranoid sceptic, it is impossible to escape the ubiquitous impact of AI. Artificial Intelligence is the brains bringing together quantum computing, nanotechnology, medical technology, brain-machine interface, robotics, aerospace, 5G, Internet of Things, and more. It is amplifying human ingenuity and disrupting the foundations of healthcare, military, entertainment, education, marketing and manufacturing.Artificial Intelligence and The Future of Power argues that this AI-driven revolution will have an unequal impact on different segments of humanity. There will be new winners and losers, new haves and have-nots, resulting in an unprecedented concentration of wealth and power. After analyzing society’s vulnerabilities to the impending tsunami, the book raises troubling questions that provoke immediate debate: Is the world headed toward digital colonization by USA and China? Will depopulation become eventually unavoidable?Artificial Intelligence and The Future of Power is a wakeup call to action, compelling public intellectuals to be better informed and more engaged. It educates the social segments most at risk and wants them to demand a seat at the table where policies on Artificial Intelligence are being formulated.

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

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     From 1941 to 1944, Bignor Manor, a farmhouse in Sussex provided board and lodging for men and women of the French Resistance before they were flown by moonlight into occupied France. Barbara Bertram, whose husband was a conducting officer for the Secret Intelligence Service (MI6), became hostess for these daring agents and their pilots during their brief stopovers in their house. But who were these men and women that passed through the Bertram’s house? And what activities did they conduct whilst in France that meant that so many of them never returned? Edward Wake-Walker charts the experiences of numerous agents, such as Gilbert Renault, Christian Pineau and Marie-Madeleine Fourcade, and the networks of operatives that they created that provided top-secret intelligence on German defences and naval bases, U-boats, as well as Hitler’s devastating new weapons, the V-1 and V-2 flying bombs. A House For Spies provides fascinating insight into the lives of SIS agents and their Lysander pilots who provided invaluable intelligence to Allied forces. This is a much-forgotten aspect of the Second World War that is only now being told by Edward Wake-Walker. “Utterly fascinating, very moving and funny. I couldn't have enjoyed it more.” — Hugh Grant “Edward Wake-Walker's meticulously researched chronicles of desperate resistance, audacity, duty, determination and daring are a valuable addition to the history of World War II” — Bel Mooney, Daily Mail “It kept me up at night as I wanted to know what happened to all the various characters [brought] so admirably back to life” — Russell England, Director of Bletchley Park: Codebreaking's Forgotten Genius and Operation Mincemeat

    Each idiom comes with a detailed description, example code showing the "wrong" way to do it, and code for the idiomatic, "Pythonic" alternative. *This version of the book is for Python 2.7.3+. There is also a Python 3.3+ version available.* "Writing Idiomatic Python" contains the most common and important Python idioms in a format that maximizes identification and understanding. Each idiom is presented as a recommendation to write some commonly used piece of code. It is followed by an explanation of why the idiom is important. It also contains two code samples: the "Harmful" way to write it and the "Idiomatic" way. * The "Harmful" way helps you identify the idiom in your own code. * The "Idiomatic" way shows you how to easily translate that code into idiomatic Python. This book is perfect for you: * If you're coming to Python from another programming language * If you're learning Python as a first programming language * If you're looking to increase the readability, maintainability, and correctness of your Python code What is "Idiomatic" Python? Every programming language has its own idioms. Programming language idioms are nothing more than the generally accepted way of writing a certain piece of code. Consistently writing idiomatic code has a number of important benefits: * Others can read and understand your code easily * Others can maintain and enhance your code with minimal effort * Your code will contain fewer bugs * Your code will teach others to write correct code without any effort on your part

     Readers will gain an understanding not only of how to use the system, its components, and the programs, but also how these fit into the total environment.

    With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora

    Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment. Artificial intelligence and machine learning are cited as the most important modern business trends to drive success. It is used in areas ranging from banking and finance to social media and marketing. This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries. This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others. Best-selling author and renowned AI expert Bernard Marr reveals how machine learning technology is transforming the way companies conduct business. This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution. Each case study provides a comprehensive overview, including some technical details as well as key learning summaries: Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations Expand your knowledge of recent AI advancements in technology Gain insight on the future of AI and its increasing role in business and industry Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the transformative power of technology in 21st century commerce.

    Each chapter introduces a new bad actor in the history of warfare, details his conquests, and features weapons and fortifications to defend against him and his minions. Clear step-by-step instructions, diagrams, and photographs show how to build a dozen projects, including “Da Vinci’s Catapult,” “Carpini’s Crossbow,” a “Crusader-Proof Moat,” “Alexander’s Tortoise,” and the “Cheval-de-frise.” With a strong emphasis on safety, the book also gives tips on troubleshooting, explains the physics behind many of the projects, and shows where to buy the materials. By the time they’ve reached the last page, at-home defenders everywhere will have succeeded in creating a fully fortified home.