The clarity and eloquence of the presentation make it popular with teachers and students alike. The text aims to expand the reader's view of the field and to present standard material in a novel way. All of the most important topics in the field are covered with a fresh perspective, including iterative methods for systems of equations and eigenvalue problems and the underlying principles of conditioning and stability. Presentation is in the form of 40 lectures, which each focus on one or two central ideas. The unity between topics is emphasized throughout, with no risk of getting lost in details and technicalities. The book breaks with tradition by beginning with the QR factorization - an important and fresh idea for students, and the thread that connects most of the algorithms of numerical linear algebra.

    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.

    The book's patient explanations are written in an informal, non-intimidating style. To underscore the relevance of mathematics to economics, the author allows the economist's analytical needs to motivate the study of related mathematical techniques; he then illustrates these techniques with appropriate economics models. Graphic illustrations often visually reinforce algebraic results. Many exercise problems serve as drills and help bolster student confidence. These major types of economic analysis are covered: statics, comparative statics, optimization problems, dynamics, and mathematical programming. These mathematical methods are introduced: matrix algebra, differential and integral calculus, differential equations, difference equations, and convex sets.

    Subsequent sections deal with integrating factors; dilution and accretion problems; linearization of first order systems; Laplace Transforms; Newton's Interpolation Formulas, more.

    In the Fourth Edition CALCULUS, EARLY TRANSCENDENTALS these functions are introduced in the first chapter and their limits and derivatives are found in Chapters 2 and 3 at the same time as polynomials and other elementary functions. In this Fourth Edition, Stewart retains the focus on problem solving, the meticulous accuracy, the patient explanations, and the carefully graded problems that have made these texts word so well for a wide range of students. All new and unique features in CALCULUS, FOURTH EDITION have been incorporated into these revisions also.

    It provides a simple, thorough survey of elementary topics, starting with set theory and advancing to metric and topological spaces, connectedness, and compactness. 1975 edition.

    Traditional mathematics teaching is largely about solving exactly stated problems exactly, yet life often hands us partly defined problems needing only moderately accurate solutions. This engaging book is an antidote to the rigor mortis brought on by too much mathematical rigor, teaching us how to guess answers without needing a proof or an exact calculation.In Street-Fighting Mathematics, Sanjoy Mahajan builds, sharpens, and demonstrates tools for educated guessing and down-and-dirty, opportunistic problem solving across diverse fields of knowledge--from mathematics to management. Mahajan describes six tools: dimensional analysis, easy cases, lumping, picture proofs, successive approximation, and reasoning by analogy. Illustrating each tool with numerous examples, he carefully separates the tool--the general principle--from the particular application so that the reader can most easily grasp the tool itself to use on problems of particular interest. Street-Fighting Mathematics grew out of a short course taught by the author at MIT for students ranging from first-year undergraduates to graduate students ready for careers in physics, mathematics, management, electrical engineering, computer science, and biology. They benefited from an approach that avoided rigor and taught them how to use mathematics to solve real problems.Street-Fighting Mathematics will appear in print and online under a Creative Commons Noncommercial Share Alike license.

    Highly recommended for graduate-level libraries.' ChoiceThis highly successful text, which first appeared in the year 1972 and has continued to be popular ever since, has now been brought up-to-date by incorporating the remarkable developments in the field of 'phase transitions and critical phenomena' that took place over the intervening years. This has been done by adding three new chapters (comprising over 150 pages and containing over 60 homework problems) which should enhance the usefulness of the book for both students and instructors. We trust that this classic text, which has been widely acclaimed for its clean derivations and clear explanations, will continue to provide further generations of students a sound training in the methods of statistical physics.

    “Big data” refers to our burgeoning ability to crunch vast collections of information, analyze it instantly, and draw sometimes profoundly surprising conclusions from it. This emerging science can translate myriad phenomena—from the price of airline tickets to the text of millions of books—into searchable form, and uses our increasing computing power to unearth epiphanies that we never could have seen before. A revolution on par with the Internet or perhaps even the printing press, big data will change the way we think about business, health, politics, education, and innovation in the years to come. It also poses fresh threats, from the inevitable end of privacy as we know it to the prospect of being penalized for things we haven’t even done yet, based on big data’s ability to predict our future behavior.In this brilliantly clear, often surprising work, two leading experts explain what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. Big Data is the first big book about the next big thing.www.big-data-book.com

    Offering inspiration and intellectual delight, the problems throughout the book encourage students to express their ideas in writing to explain how they conceive problems, what conjectures they make, and what conclusions they reach. Applying specific techniques and strategies, readers will acquire a solid understanding of the fundamental concepts and ideas of number theory.

    Features exceptionally clear explanations and descriptions, step-by-step examples, more than 50 practical applications, over 2000 easy-to-challenging practice problems, and comprehensive coverage of essentials. PSpice, OrCAd version 9.2 Lite Edition, Multisims 2001 version of Electronics Workbench, and MathCad software references and examples are used throughout. Computer programs (C++, BASIC and PSpice) are printed in color, as they run, at the point in the book where they are discussed. Current and Voltage. Resistance. Ohm's Law, Power, and Energy. Series Circuits. Parallel Circuits. Series-Parallel Networks. Methods of Analysis & Selected Topics. Network Theorems. Capacitors. Magnetic Circuits. Inductors. Sinusodial Alternating Waveforms. The Basic Elements and Phasors. Series and Parallel ac Circuits. Series-Parallel ac Networks. Methods of Analysis and Related Topics. Network Theorems (ac). Power (ac). Resonance. Transformers. Polyphase Systems. Decibels, Filters, and Bode Points. Pulse Waveforms and the R-C Response. Nonsinusodial Circuits. System Analysis: An Introduction. For those working in electronic technology.

    A series of formal models are used to present and analyze important macroeconomic theories. The theories are supplemented by examples of relevant empirical work, which illustrate the ways that theories can be applied and tested. This well-respected and well-known text is unique in the marketplace.

    Graphs are not included, but both the graphs and the concepts behind them are explained; only basic math is included, and you can even skim over the math and still come away with an understanding of the concepts; statistics is not included at all.BASIC ECONOMICS FOR STUDENTS AND NON-STUDENTS ALIKE is an easy way to learn concepts relating to economics and the economy. It is a product of thousands of hours spent online, teaching basic concepts in economics to hundreds of students worldwide over the course of the past several years. From back and forth communications, I have discovered the explanations for the concepts that students find easiest to understand, as well as the areas that most often get misunderstood and under-emphasized.I have worked with students located throughout the United States and from many different countries, on six different continents; students from many different school systems with different points of emphasis; students with different levels of knowledge, different backgrounds, and different levels of interest in the subject. I have received numerous comments and testimonials regarding the teaching methods that I incorporate in BASIC ECONOMICS FOR STUDENTS AND NON-STUDENTS ALIKE.The subject matter included in BASIC ECONOMICS FOR STUDENTS AND NON-STUDENTS ALIKE comes from a compilation of many different textbooks at the introductory and intermediate levels. My goal was to include every subject in economics that normally will be found in an introductory level textbook of economics, microeconomics, or macroeconomics. Since different school systems, different classroom instructors, and different textbooks cover a slightly different combination of topics, BASIC ECONOMICS FOR STUDENTS AND NON-STUDENTS ALIKE is a little more comprehensive than most single introductory textbooks of economics. Some of the topics will be found in introductory classes in some schools, but in intermediate-level classes in other schools.

    Men like Andrew Carnegie built empires like Carnegie Steel, and financiers like J.P. Morgan merged and consolidated them. The era also made names like Astor, Cooke, and Vanderbilt instantly recognizable across the globe. Over time, the unfathomable wealth generated by the businesses made the individuals on top incredibly rich, and that in turn led to immense criticism and an infamous epithet used to rail against them: robber barons. Dozens of men were called “robber barons”, but few of them were as notorious as Cornelius Vanderbilt, who also happened to be one of the nation’s first business titans. Vanderbilt was a railroad and shipping magnate at a time that the industry was almost brand new, but he rode his success to become one of the richest and most powerful men in American history. When historians are asked to name the richest man in history, a name that often pops up is that of John D. Rockefeller, who co-founded Standard Oil and turned it into the first real trust in the United States. Rockefeller had been groomed ambitiously by a huckster father nicknamed “Devil Bill”, who was just as willing to cheat his son as an unsuspecting public, and John certainly chased his dreams of living long and large. Rockefeller forged his empire in the first few decades of his life and nearly worked himself to death by the time he was 50, which helped compel him to retire for the last several decades of his life. At one point, Rockefeller’s wealth was worth more than 1.5% of the entire country’s gross domestic product, and by adjusting for inflation, he is arguably the richest man in American history if not world history. When robber barons across America took the reins of vast industries, they needed financing, and many of them turned to the most famous banker of all: John Pierpont Morgan. It was J.P. Morgan who bankrolled the consolidation of behemoth corporations across various industries, including the merging of Edison General Electric and Thomson-Houston Electric Company, which subsequently became General Electric, still known simply as GE across the world today. Similarly, he financed Federal Steel Company and consolidated various other steel businesses to help form the United States Steel Corporation. While critics complained about the outsized influence that these gigantic businesses had, Morgan’s massive wealth also gave him unprecedented power in the financial sector and the ability to deal with politicians. In fact, Morgan played an important part in the Panic of 1907 and the subsequent decision to create the Federal Reserve as a monetary oversight. Ironically, one of America’s most famous robber barons, Andrew Carnegie, epitomized the American Dream, migrating with his poor family to America in the mid-19th century and rising to the top of the business world in his adopted country. A prodigious writer in addition to his keen sense of business, Carnegie was one of the most outspoken champions of capitalism at a time when there was pushback among lower social classes who witnessed the great disparities in wealth; as he once put it, “Upon the sacredness of property civilization itself depends—the right of the laborer to his hundred dollars in the savings bank, and equally the legal right of the millionaire to his millions.

    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details