Forty-one Experiential Exercises, and 41 cases are included. Topics covered include corporate culture, organizational structure, marketing concepts, financial tools and techniques, strategy implementation issues, as well as extensive coverage of global issues, concerns and idiosyncrasies. For anyone interested in the fields of Strategic Management, Strategy, and Business Policy.

    Throughout, the emphasis is on how IT provides organizations with strategic advantage by facilitating problem solving, increasing productivity and quality, improving customer service, and enabling business process reengineering. It also covers the latest real-world developments, including the introduction of applied grid computing and utility computing.

    

    Follow The Effortless Step-By-Step Instructions In The Book To Completely Get Rid Of Your Accent and speak English like a native! How to speak English fluently? Learn from the success to perfect your English pronunciation, your tone, your flow, your expression, and everything you need to speak English naturally just like a native speaker would to make you speak English like a native speaker. So that instead of saying "how learn English" --> you would naturally say "how to learn English." What Level Of English Fluency Will You Achieve: Short Answer: Native. How to speak English like a native? Effortlessly Follow The Step-By-Step Instructions In The Book To Achieve The Highest Level Of Fluency, Making You Speak English Like A Native Speaker. The Author Is A Successful English Learner: This Book Is Written By An English Learner Who Successfully Learned To Speak English Like A Native Speaker. While Knowing Only Little English, The Author Started To Learn English Fluency At The Age Of 20 And Successfully Turned His Broken English Into Fluent English In Just 6 Months. "English spoken" It's actually "Spoken English." In The Continuing Effortless Practices, He Then Perfected His Fluency, Making Him Speak English Like A Native. "How speak English?" Well, it's "How to speak English?" You'll naturally speak it in the right way after following the step-by-step instructions in the book. Likewise, "English grammer," "Grammar English." it's actually "English grammar." Use For English And Any Other Language: The Author Founded The Myfluentenglish Formula Which He Used To Perfect His Speaking In English. Using The Same Formula, The Author Also Perfected His Speaking In Two Other Languages. This Formula Is Designed To Make You Speak English Like A Native. The Formula Is Also Designed To Make You Speak Any Other Language Like A Native. Just Follow The Step-By-Step Instructions In The Book. The best way to speak English. It's OK to type "The best way to speak Englosh." One out of 100 people would type Englosh instead of English. Learn English tenses, English verbs, grammar, vocabulary, fluency and everything you need to speak English like a native naturally by following the instructions in Talk English! What Comes With It: Talk English - How To Learn From The Success To Speak English Like A Native, A Step-By-Step Guide To Learn Spoken English + Bonus 1: Let's Get Started - 3 Strategies To Start Speaking English Immediately + Bonus 2: Let's Talk - How To Start A Conversation In English + Bonus 3: Vocabulary Built Solid - 6 Ways To Build A Rock Solid Vocabulary + Bonus 4: Free Lifetime Access To Native Study Materials. What readers are saying: THIS is the book every English learner NEEDS to read. Crystal F. Canada After 2 weeks, the change is significant. Most importantly I think like a native speaker starting building a little bit in my subconsciousness. Dexuan L. China How I can speak English so fast without accent? With your tips I can do it. I will recommend to my friends with broken English because I think your book is very helpful for me and other people like me to learn speak fluent English. Thank you so much! Farah A. Iran The book provides the readers a workable way on how to get to speak fluent English. It's simple, straightforward, and most important of all, its focused and not too way long to cost the readers too much time. Joanne L.

    

    With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems.Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution.Model data with the Cypher query language and property graph modelLearn best practices and common pitfalls when modeling with graphsPlan and implement a graph database solution in test-driven fashionExplore real-world examples to learn how and why organizations use a graph databaseUnderstand common patterns and components of graph database architectureUse analytical techniques and algorithms to mine graph database information

    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, 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.

    Introducing students to the many facets of neural networks, this text provides many case studies to illustrate their real-life, practical applications.

    KEY TOPICS: Coverage begins with an optional review of key concepts--such as properties of compound semiconductor, quantum mechanics, semiconductor statistics, carrier transport properties, optical processes, and junction theory--then progress gradually through more advanced topics. The Second Edition has been both updated and expanded to include the recent developments in the field.

    Turing Mathematician Alan Turing invented an imaginary computer known as the Turing Machine; in an age before computers, he explored the concept of what it meant to be "computable," creating the field of computability theory in the process, a foundation of present-day computer programming.The book expands Turing's original 36-page paper with additional background chapters and extensive annotations; the author elaborates on and clarifies many of Turing's statements, making the original difficult-to-read document accessible to present day programmers, computer science majors, math geeks, and others.Interwoven into the narrative are the highlights of Turing's own life: his years at Cambridge and Princeton, his secret work in cryptanalysis during World War II, his involvement in seminal computer projects, his speculations about artificial intelligence, his arrest and prosecution for the crime of "gross indecency," and his early death by apparent suicide at the age of 41.

     Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

    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.

    GRAPH THEORY WITH APPLICATIONS TO ENGINEERING AND COMPUTER SCIENCE-PHI-DEO, NARSINGH-1979-EDN-1

    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.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.