What you need to learn to pass a Cisco certification exam such as CCNA and what you need to know to survive in the real world are two very different things. The strategies that this book offers weren 't on the exam, but they 're exactly what you need to do your job well.Network Warrior takes you step by step through the world of hubs, switches, firewalls, and more, including ways to troubleshoot a congested network, and when to upgrade and why. Along the way, you 'll gain an historical perspective of various networking features, such as the way Ethernet evolved. Based on the author 's own experience as well as those he worked for and with, Network Warrior is a Cisco-centric book, focused primarily on the TCP/IP protocol and Ethernet networks -- the realm that Cisco Systems now dominates. The book covers: The type of networks now in use, from LANs, WANs and MANs to CANsThe OSI Model and the layers involved in sending data Hubs, repeaters, switches, and trunks in practice Auto negotiation and why it 's a common problem in network slowdowns Route maps, routing protocols, and switching algorithms in Cisco routers The resilient Ethernet -- how to make things truly redundant Cisco 6500 multi-layer switches and the Catalyst 3750 switch Telecom nomenclature -- why it 's different from the data world T1 and DS3 Firewall theory, designing access lists, authentication in Cisco devices Server load balancing technology Content switch module in action Designing QOS and what QOS does not do IP design and subnetting made easy The book also explains how to sell your ideas to management, how networks become a mess as a company grows, and why change control is your friend. Network Warrior will help network administrators and engineers win the complex battles they face every day.

    But no book addresses how to harness the incredible power of social media to make a difference. "The Dragonfly Effect" shows you how to tap social media and consumer psychological insights to achieve a single, concrete goal. Named for the only insect that is able to move in any direction when its four wings are working in concert, this bookReveals the four "wings" of the Dragonfly Effect-and how they work together to produce colossal resultsFeatures original case studies of global organizations like the Gap, Starbucks, Kiva, Nike, eBay, Facebook; and start-ups like Groupon and COOKPAD, showing how they achieve social good and customer loyaltyLeverage the power of design thinking and psychological research with practical strategiesReveals how everyday people achieve unprecedented results-whether finding an almost impossible bone marrow match for a friend, raising millions for cancer research, or electing the current president of the United States"The Dragonfly Effect" shows that you don't need money or power to inspire seismic change.

    This work represents the views of a mathematician on the analogies between computing machines and the living human brain.

    If you're executign strategy and want to know how the Web is changing business, this is the book you need.

    

    Readers will continue to enjoy the current, concise, and straightforward approach to the field that made previous editions so popular. They'll understand the experimental observations that underlie the science of immunology at the molecular, cellular, and whole organism level-and explore the conclusions that can be drawn from those observations.The smart way to study!Elsevier titles with STUDENT CONSULT will help you master difficult concepts and study more efficiently in print and online! Perform rapid searches. Integrate bonus content from other disciplines. Download text to your handheld device. And a lot more. Each STUDENT CONSULT title comes with full text online, a unique image library, case studies, USMLE style questions, and online note-taking to enhance your learning experience.Highlights the implications of immunologic science for the management of human disease.Features over 365 full-color illustrations that highlight important processes and concepts, and bring the three-dimensional structures of molecules to life. Offers a wealth of reader-friendly features including major concepts highlighted in bold italics - at-a-glance boxes that present specialized information and explanations of current technological approaches - a wealth of tables that summarize key information - a glossary of terms - a CD Molecule Appendix that reflects the latest properties of known CD Molecules - an appendix on immunologic laboratory techniques - and more.Includes free access to a full-text online version, a downloadable collection of full-color images, sophisticated immunologic animations, integration links to bonus content in other STUDENT CONSULT titles, content clipping for handhelds, and more - all through the STUDENT CONSULT website at www.studentconsult.com.Incorporates the newest understandings of basic science as well as the newest research findings.Includes new information on the organization of lymphoid organs and mechanisms of innate immunity.Presents enhanced, more straightforward explanations of subjects such as of antigen-presenting cells and the biology of T cell activation.

    I always live without knowing. That is easy. How you get to know is what I want to know."--Richard P. FeynmanNobel Prize-winning physicist Richard P. Feynman (1918-88) was that rarest of creatures--a towering scientific genius who could make himself understood by anyone and who became as famous for the wit and wisdom of his popular lectures and writings as for his fundamental contributions to science. "The Quotable Feynman" is a treasure-trove of this revered and beloved scientist's most profound, provocative, humorous, and memorable quotations on a wide range of subjects.Carefully selected by Richard Feynman's daughter, Michelle Feynman, from his spoken and written legacy, including interviews, lectures, letters, articles, and books, the quotations are arranged under two dozen topics--from art, childhood, discovery, family, imagination, and humor to mathematics, politics, science, religion, and uncertainty. These brief passages--about 500 in all--vividly demonstrate Feynman's astonishing yet playful intelligence, and his almost constitutional inability to be anything other than unconventional, engaging, and inspiring. The result is a unique, illuminating, and enjoyable portrait of Feynman's life and thought that will be cherished by his fans at the same time that it provides an ideal introduction to Feynman for readers new to this intriguing and important thinker.The book features a foreword in which physicist Brian Cox pays tribute to Feynman and describes how his words reveal his particular genius, a piece in which cellist Yo-Yo Ma shares his memories of Feynman and reflects on his enduring appeal, and a personal preface by Michelle Feynman. It also includes some previously unpublished quotations, a chronology of Richard Feynman's life, some twenty photos of Feynman, and a section of memorable quotations about Feynman from other notable figures.Features: Approximately 500 quotations, some of them previously unpublished, arranged by topicA foreword by Brian Cox, reflections by Yo-Yo Ma, and a preface by Michelle FeynmanA chronology of Feynman's lifeSome twenty photos of FeynmanA section of quotations about Feynman from other notable figures Some notable quotations of Richard P. Feynman: "The thing that doesn't fit is the most interesting.""Thinking is nothing but talking to yourself inside.""It is wonderful if you can find something you love to do in your youth which is big enough to sustain your interest through all your adult life. Because, whatever it is, if you do it well enough (and you will, if you truly love it), people will pay you to do what you want to do anyway.""I'd hate to die twice. It's so boring."

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    You NEED ME") • And the Meek Shall Manipulate the Earth (EVER KNOW MUCH, AND YOU'll. GET FURTHER) • How to Pick a Person Apart and Discover His Real Motives and Feelings • The Guernsey vs. the Brahma (WHY A "POSITIVE ATTITUDE" IS LIKE A GIGOLO) • How to Avoid Being Taken in Financial Dealings (WATCH WHERE THE MONEY IS) • Manipulating a Person's Thinking (KNOW WHOSE MIND YOU CAN CHANGE, AND WHEN IT'S BETTER TO SHUT UP) • Win Ninety-five Percent of Your Arguments! (RULE OF THUMB: LET SLEEPING DOGS LIE IF THEY DON'T AFFECT YOUR VITAL INTERESTS) • Manipulate a Person Against Their Will and Make Them Like It! (THE UNARGUE TECHNIQUE) • A Little Pressure Has Its Place-But Seldom • If All Else Fails, Try the Dirty Way. • How to Squelch Ingratitude Before It Starts. • What You Get Out of Using People for Your Own Selfish Ends?Unsettling, candid, and very informative.

    In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they’ve recovered from nasty data problems.From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line? Bad data is data that gets in the way. This book explains effective ways to get around it.Among the many topics covered, you’ll discover how to:Test drive your data to see if it’s ready for analysisWork spreadsheet data into a usable formHandle encoding problems that lurk in text dataDevelop a successful web-scraping effortUse NLP tools to reveal the real sentiment of online reviewsAddress cloud computing issues that can impact your analysis effortAvoid policies that create data analysis roadblocksTake a systematic approach to data quality analysis

    Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques.The book deals with some of the most important and challenging areas of programming and computer science, but in a highly pedagogic and readable manner. The book covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others himself.

    Here the texts of two of Schr�dinger's most famous lecture series are made available again. In the first, entitled Nature and the Greeks, Schr�dinger offers a historical account of the scientific world picture. In the second, called Science and Humanism, he addresses fundamental questions about the link between scientific and spiritual matters. As Roger Penrose confirms, these are the profound thoughts of a great mind, and as relevant today as when they were first published in the 1950s.

    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.

    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.

    "Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, the author uses the flexible Python programming language to show how to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.