The author successfully introduces Maxwell's equations, wave propagation, network analysis, and design principles as applied to modern microwave engineering. A considerable amount of material in this book is related to the design of specific microwave circuits and components, for both practical and motivational value. It also presents the analysis and logic behind these designs so that the reader can see and understand the process of applying the fundamental concepts to arrive at useful results. The derivations are well laid out and the majority of each chapter's formulas are displayed in a nice tabular format every few pages. This Third Edition offers greatly expanded coverage with new material on: Noise; Nonlinear effects; RF MEMs; transistor power amplifiers; FET mixers; oscillator phase noise; transistor oscillators and frequency multiplier.

    In an instructive and conversational tone, Cody clearly explains how to program SAS, illustrating with one or more real-life examples and giving a detailed description of how the program works.

    Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

    Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

    Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

    This highly anticipated fourth edition of the most ...Download Link : readmeaway.com/download?i=0128042915            0128042915 Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF by Ian H. WittenRead Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF from Morgan Kaufmann,Ian H. WittenDownload Ian H. Witten's PDF E-book Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)

    Thoroughly updated, this edition offers new, faster techniques for solving differential equations generated by the emergence of high-speed computers.

    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.

    Digital technologies—with hardware, software, and networks at their core—will in the near future diagnose diseases more accurately than doctors can, apply enormous data sets to transform retailing, and accomplish many tasks once considered uniquely human.In The Second Machine Age MIT’s Erik Brynjolfsson and Andrew McAfee—two thinkers at the forefront of their field—reveal the forces driving the reinvention of our lives and our economy. As the full impact of digital technologies is felt, we will realize immense bounty in the form of dazzling personal technology, advanced infrastructure, and near-boundless access to the cultural items that enrich our lives.Amid this bounty will also be wrenching change. Professions of all kinds—from lawyers to truck drivers—will be forever upended. Companies will be forced to transform or die. Recent economic indicators reflect this shift: fewer people are working, and wages are falling even as productivity and profits soar.Drawing on years of research and up-to-the-minute trends, Brynjolfsson and McAfee identify the best strategies for survival and offer a new path to prosperity. These include revamping education so that it prepares people for the next economy instead of the last one, designing new collaborations that pair brute processing power with human ingenuity, and embracing policies that make sense in a radically transformed landscape.A fundamentally optimistic book, The Second Machine Age alters how we think about issues of technological, societal, and economic progress.

    In this entertaining and ingenious book, Blastland reveals how in our quest to make the world more understandable, we lose sight of how unexplainable it often is. The result - from GDP figures to medicine - is that experts know a lot less than they think. Filled with compelling stories from economics, genetics, business, and science, The Hidden Half is a warning that an explanation which works in one arena may not work in another. Entertaining and provocative, it will change how you view the world.

    It is important for every person to save for one’s retirement as one can expect to live for twenty years after one retires as life expectancy of an Indian is going up steadily due to lower infant mortality and better medical care. There is a complete absence of social security safety net for most Indians today, even for those working in Government sector, there is no inflation adjusted pension available anymore. I have tried to simplify the advantages and disadvantages involved in investing your savings in various asset classes. I have deliberately left out two of the most popular forms of investment among middle class Indians 1) Life insurance 2) Real estate Let us consider life insurance first most of us confuse insurance as an instrument of savings, it is not. We have this wrong view because of the tax breaks given to income tax assesses by the Central Government. Insurance is a product that mitigates risk and is sold by the rich to the middle class and is always skewed in the favour of the insurer rather than the insured. A substantial portion of the total money invested by you goes towards paying agent’s commission and premium for insuring you for the risk of mortality. The balance left out is invested in government securities and other securities. Hence the amount of money invested out of the total premium paid is less than half paid by the insurer. The return on money invested by the policy holder is less than half of the money he would have earned either in bonds or fixed deposits. A person who needs insurance is a person whose family will need support in the event of his untimely death. Alternately insurance is required for a person who has debt in form of mortgage and does not want to burden his family in the event of his passing. The product which covers these risks is called term insurance. One should not buy insurance to avoid taxes as there is better tax saving tools available. Real estate is also considered as a good investment by several retail investors but nothing can be further from the truth. Nobody makes money by buying plots in the middle of nowhere. The easy availability of mortgages from the nineties and the tax breaks given by the Central Government on housing loans has created an unparalleled boom in the residential market. There is now a painful correction process under way in that sector. The price of land is reflexively connected to availability of money. The lower the cost of money, greater the returns in real estate. Buying plots in the middle of nowhere is similar to buying lottery tickets as investment. Land cannot be liquidated immediately into cash at a short notice to meet urgent requirements. Cost of maintenance and protection of real estate from illegal occupation is prohibitive and time consuming. Verification of title deeds to the property is a complex process and needs sound legal advice. You should have a house to live and another to collect rent as rent is equivalent of inflation adjusted pension. The return on investment generated in the three different asset classes over 25 years would be in the following order 1) Equities 2) Gold and finally 3) Debt instruments. I enjoyed writing this book as a companion volume to my first book. It is my fond hope that you enjoy reading this book.

    Like its sister textbook, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is enhanced by a cast of characters to help the reader on their way, hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more.

    Erin recounts her path of achievement starting as a promising young student and athlete and, ultimately, how she allowed her career and its demands to become the center of her life. She sacrificed all other priorities and relationships along the way, throwing work-life balance to the wind. The story reveals the subtleties of the everyday decisions that led collectively to a work-centric existence over a twenty-year career. Set against the backdrop of the dramatic circumstances at Lehman Brothers in 2008, Erin discloses her own struggle as events spiraled out of control. Ultimately, her resignation from her executive role prior to the Lehman bankruptcy resulted in a devastating personal crisis as her career crumbled revealing no foundation beneath it. We learn of the journey back to change her life with a semblance of present day peace and happiness. Full Circle provides a unique inside and emotional perspective of the sacrifices Erin made to achieve extreme career success and the self-awareness required to return to being the fundamentally grounded person she was as a child.