It emphasizes forms suitable for students and researchers whose interest lies in solving equations rather than in general theory. Solutions to odd-numbered problems appear at the end. 1957 edition.

    The authors provide precise definitions and full proofs of results, sacrificing generalities and limiting the scope of the material in order to do so. The text is organized in four parts: strategic games, extensive games with perfect information, extensive games with imperfect information, and coalitional games. It includes over 100 exercises. Solution ManualTable of Contents, Errata, and more...

    Python is easy to learn, and it's perfect for exploring topics like statistics, geometry, probability, and calculus. You’ll learn to write programs to find derivatives, solve equations graphically, manipulate algebraic expressions, even examine projectile motion.Rather than crank through tedious calculations by hand, you'll learn how to use Python functions and modules to handle the number crunching while you focus on the principles behind the math. Exercises throughout teach fundamental programming concepts, like using functions, handling user input, and reading and manipulating data. As you learn to think computationally, you'll discover new ways to explore and think about math, and gain valuable programming skills that you can use to continue your study of math and computer science.If you’re interested in math but have yet to dip into programming, you’ll find that Python makes it easy to go deeper into the subject—let Python handle the tedious work while you spend more time on the math.

    The book will also be extremely useful for those preparing for other MBA entrance examinations like XAT, SNAP, CMAT, NMAT, etc. Quantitative Aptitude is quite challenging component of the CAT question paper and the other mentioned MBA entrance examinations. In his inimitable style, Arun Sharma, an acknowledged authority on the topic, provides a comprehensive package of theory and practice problems to enable aspirants to attempt questions with extra speed and confidence.

    

    For courses in computer organization and architecture, this text provides a clear, comprehensive presentation of the organization and architecture of contemporary computers.

    Just read this till the end You don’t have to buy this book. Just read this till end & you will learn something that will change the way you do math forever. Warning: I am revealing this secret only to the first set of readers who will buy this book & plan to put this secret back inside the book once I have enough sales. So read this until the very end while you still can.School taught you the wrong way to do mathThe way you were taught to do math, uses a lot of working memory. Working memory is the short term memory used to complete a mental task. You struggle because trying to do mental math the way you were taught in school, overloads your working memory. Let me show you what I mean with an example:Try to multiply the 73201 x 3. To do this you multiply the following:1 x 3 =0 x 3 =2 x 3 =3 x 3 =7 x 3 =This wasn’t hard, & it might have taken you just seconds to multiply the individual numbers. However, to get the final answer, you need to remember every single digit you calculated to put them back together. It takes effort to get the answer because you spend time trying to recall the numbers you already calculated. Math would be easier to do in your head if you didn’t have to remember so many numbers. Imagine when you tried to multiply 73201 x 3, if you could have come up with the answer, in the time it took you to multiply the individual numbers. Wouldn’t you have solved the problem faster than the time it would have taken you to punch in the numbers inside a calculator? Do the opposite of what you were taught in schoolThe secret of doing mental math is to calculate from left to right instead of from right to left. This is the opposite of what you were taught in school. This works so well because it frees your working memory almost completely. It is called the LR Method where LR stands for Left to Right.Lets try to do the earlier example where we multiplied 73201 x 3. This time multiply from left to right, so we get:7 x 3 = 213 x 3 = 93 x 2 = 60 x 3 = 03 x 1 = 3Notice that you started to call out the answer before you even finished the whole multiplication problem. You don’t have to remember a thing to recall & use later. So you end up doing math a lot faster. The Smart ChoiceYou could use what you learnt & apply it to solve math in the future. This might not be easy, because we just scratched the surface. I've already done the work for you. Why try to reinvent the wheel, when there is already a proven & tested system you can immediately apply. This book was first available in video format & has helped 10,000+ students from 132 countries. It is available at ofpad.com/mathcourse to enroll. This book was written to reach students who consume the information in text format. You can use the simple techniques in this book to do math faster than a calculator effortlessly in your head, even if you have no aptitude for math to begin with.Imagine waking up tomorrow being able to do lightning fast math in your head. Your family & friends will look at you like you are some kind of a genius. Since calculations are done in your head, you will acquire better mental habits in the process. So you will not just look like a genius. You will actually be one. Limited Time BonusWeekly training delivered through email for $97 is available for free as a bonus at the end of this book for the first set of readers. Once we have enough readers, this bonus will be charged $97. Why Price Is So LowThis book is priced at a ridiculous discount only to get our first set of readers. When we have enough readers the price will go up.

    The heart of this creative process lies in an analogy between proving mathematical theorems by induction and designing combinatorial algorithms. The book contains hundreds of problems and examples. It is designed to enhance the reader's problem-solving abilities and understanding of the principles behind algorithm design.

    The calculus is not a hard subject and I prove this through an easy to read and obvious approach spanning only 100 pages. I have written this book with the following type of student in mind; the non-traditional student returning to college after a long break, a notoriously weak student in math who just needs to get past calculus to obtain a degree, and the garage tinkerer who wishes to understand a little more about the technical subjects. This book is meant to address the many fundamental thought-blocks that keep the average 'mathaphobe' (or just an interested person who doesn't have the time to enroll in a course) from excelling in mathematics in a clear and concise manner. It is my sincerest hope that this book helps you with your needs.Show more Show less

    That is, it is a piece of bad logic. Just as it is a good idea to avoid eating bad food, it is also a rather good idea to avoid bad reasoning. Unfortunately, bad reasoning is all too common—it pours out of the television and infests the web like an army of venomous spiders. Perhaps even worse than the fallacies inflicted from the outside are self-inflicted fallacies. These can lead people to make poor decisions about matters great and small.Fortunately, there is a defense against bad reasoning, namely knowledge. This concise book provides the reader with definitions and examples of seventy-six common fallacies—the knowledge a person needs to defend herself in a world awash in fallacies.In addition to combining the content of my 42 Fallacies and 30 More Fallacies, this book features some revisions as well as a new section on common formal fallacies. The focus is on providing the reader with definitions and examples of these common fallacies rather than being a handbook on winning arguments or a text on general logic.The book presents the following 73 informal fallacies:Accent, Fallacy ofAccident, Fallacy ofAd HominemAd Hominem Tu QuoqueAmphiboly, Fallacy ofAnecdotal Evidence, Fallacy OfAppeal to the Consequences of a BeliefAppeal to Authority, FallaciousAppeal to BeliefAppeal to Common PracticeAppeal to EmotionAppeal to EnvyAppeal to FearAppeal to FlatteryAppeal to Group IdentityAppeal to GuiltAppeal to NoveltyAppeal to PityAppeal to PopularityAppeal to RidiculeAppeal to SpiteAppeal to TraditionAppeal to SilenceAppeal to VanityArgumentum ad HitlerumBegging the QuestionBiased GeneralizationBurden of ProofComplex QuestionComposition, Fallacy ofConfusing Cause and EffectConfusing Explanations and ExcusesCircumstantial Ad HominemCum Hoc, Ergo Propter HocDivision, Fallacy ofEquivocation, Fallacy ofFallacious ExampleFallacy FallacyFalse DilemmaGambler’s FallacyGenetic FallacyGuilt by AssociationHasty GeneralizationHistorian’s FallacyIllicit ConversionIgnoring a Common CauseIncomplete EvidenceMiddle GroundMisleading VividnessMoving the Goal PostsOversimplified CauseOverconfident Inference from Unknown StatisticsPathetic FallacyPeer PressurePersonal AttackPoisoning the WellPositive Ad HominemPost HocProving X, Concluding YPsychologist's fallacyQuestionable CauseRationalizationRed HerringReification, Fallacy ofRelativist FallacySlippery SlopeSpecial PleadingSpotlightStraw ManTexas Sharpshooter FallacyTwo Wrongs Make a RightVictim FallacyWeak AnalogyThe book contains the following three formal (deductive) fallacies:Affirming the ConsequentDenying the AntecedentUndistributed Middle

    The book will teach you about:* Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data* Deep learning, a powerful set of techniques for learning in neural networksNeural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.

    Stanford Wong has contributed to Professional Blackjack .Wong is one of the country's leading gambling authorities.

    Now that its basketball program has fallen apart, CSU’s only claim to fame is its Gravinics Department, dedicated to the study of an obscure European country—its mythology, its extraordinarily difficult language, and especially its bizarre star poet, Henderson.Having discovered Henderson’s poetry in a trash bin, Stanley Higgs becomes the foremost scholar of the poet’s work, accepts a position at Chandler State University, achieves international academic fame, marries the Dean’s daughter, and abruptly stops talking. With all of academia convinced that Higgs is formulating a great truth, the university employs Orwellian techniques to record Higgs’s every potential utterance and to save its reputation. A feckless Gravinics language student, Samuel Grapearbor, together with his long-suffering girlfriend Julia, is hired to monitor Higgs during the day. Over endless games of checkers and shared sandwiches, a uniquely silent friendship develops. As one man struggles to grow up and the other grows old, The Grasshopper King, in all of his glory, emerges.In this debut novel about treachery, death, academia, marriage, mythology, history, and truly horrible poetry, Jordan Ellenberg creates a world complete with its own geography, obscene folklore, and absurdly endearing -characters—a world where arcane subjects flourish and the smallest swerve from convention can result in -immortality.Jordan Ellenberg was born in Potomac, Maryland in 1971. His brilliance as a mathematical prodigy led to a feature in The National Enquirer, an interview with Charlie Rose on CBS’s Nightwatch, and gold medals at the Math Olympiad in Cuba and Germany. He is now an Assistant Professor of Math at Princeton University and his column, "Do the Math," appears regularly in the online journal Slate. This is his first novel.

    It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, philosophy, cognitive science, and the health and social sciences. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable. Professor of Computer Science at the UCLA, Judea Pearl is the winner of the 2008 Benjamin Franklin Award in Computers and Cognitive Science.