Book picks similar to
Maths for Beginners by Manish Thakur
math
learning
mathematics
not-enough-ratings
Algebra - The Very Basics
Metin Bektas - 2014
This book picks you up at the very beginning and guides you through the foundations of algebra using lots of examples and no-nonsense explanations. Each chapter contains well-chosen exercises as well as all the solutions. No prior knowledge is required. Topics include: Exponents, Brackets, Linear Equations and Quadratic Equations. For a more detailed table of contents, use the "Look Inside" feature. From the author of "Great Formulas Explained" and "Physics! In Quantities and Examples".
Aha! Insight
Martin Gardner - 1978
Aha! Insight challenges the reader's reasoning power and intuition while encouraging the development of 'aha! reactions'.
M.C. Escher: Visions of Symmetry
Doris Schattschneider - 1990
It deals with one powerful obsession that preoccupied Escher: what he called "the regular division of the plane," the puzzlelike interlocking of birds, fish, lizards, and other natural forms in continuous patterns. Schattschneider asks, "How did he do it?" She answers the question by analyzing Escher's notebooks." Visions of Symmetry includes many of Escher's masterworks, as well as hundreds of lesser-known examples of his work. This new edition also features a foreward and an illustrated epilogue that reveals new information about Escher's inspiration and shows how his ideas of symmetry have influenced mathematicians, computer scientists, and contemporary artists.
All of Statistics: A Concise Course in Statistical Inference
Larry Wasserman - 2003
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.
The Art of Doing Science and Engineering: Learning to Learn
Richard Hamming - 1996
By presenting actual experiences and analyzing them as they are described, the author conveys the developmental thought processes employed and shows a style of thinking that leads to successful results is something that can be learned. Along with spectacular successes, the author also conveys how failures contributed to shaping the thought processes. Provides the reader with a style of thinking that will enhance a person's ability to function as a problem-solver of complex technical issues. Consists of a collection of stories about the author's participation in significant discoveries, relating how those discoveries came about and, most importantly, provides analysis about the thought processes and reasoning that took place as the author and his associates progressed through engineering problems.
Five Equations That Changed the World
Michael Guillen - 1995
Michael Guillen, known to millions as the science editor of ABC's Good Morning America, tells the fascinating stories behind five mathematical equations. As a regular contributor to daytime's most popular morning news show and an instructor at Harvard University, Dr. Michael Guillen has earned the respect of millions as a clear and entertaining guide to the exhilarating world of science and mathematics. Now Dr. Guillen unravels the equations that have led to the inventions and events that characterize the modern world, one of which -- Albert Einstein's famous energy equation, E=mc2 -- enabled the creation of the nuclear bomb. Also revealed are the mathematical foundations for the moon landing, airplane travel, the electric generator -- and even life itself. Praised by Publishers Weekly as "a wholly accessible, beautifully written exploration of the potent mathematical imagination," and named a Best Nonfiction Book of 1995, the stories behind The Five Equations That Changed the World, as told by Dr. Guillen, are not only chronicles of science, but also gripping dramas of jealousy, fame, war, and discovery. Dr. Michael Guillen is Instructor of Physics and Mathematics in the Core Curriculum Program at Harvard University.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Trevor Hastie - 2001
With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.
Chances Are . . .: Adventures in Probability
Michael Kaplan - 2003
All things are possible, only one thing actually happens; everything else is in the realm of probability. The twin disciplines of probability and statistics underpin every modern science and sketch the shape of all purposeful group activity- politics, economics, medicine, law, sports-giving humans a handle on the essential uncertainty of their existence. Yet while we are all aware of the hard facts, most of us still refuse to take account of probability-preferring to drive, not fly; buying into market blips; smoking cigarettes; denying we will ever age. There are some people, though-gamblers, risk buyers, forensic experts, doctors, strategists- who find probability's mass of incomplete uncertainties delightful and revelatory. "Chances Are" is their story. Combining philosophical and historical background with portraits of the men and women who command the forces of probability, this engaging, wide-ranging, and clearly written volume will be welcomed not only by the proven audiences for popular books like "E=MC2" and "The Golden Ratio" but by anyone interested in the workings of fate.
Emergence: The Connected Lives of Ants, Brains, Cities, and Software
Steven Johnson - 2001
Explaining why the whole is sometimes smarter than the sum of its parts, Johnson presents surprising examples of feedback, self-organization, and adaptive learning. How does a lively neighborhood evolve out of a disconnected group of shopkeepers, bartenders, and real estate developers? How does a media event take on a life of its own? How will new software programs create an intelligent World Wide Web? In the coming years, the power of self-organization -- coupled with the connective technology of the Internet -- will usher in a revolution every bit as significant as the introduction of electricity. Provocative and engaging, Emergence puts you on the front lines of this exciting upheaval in science and thought.
Modern abc of physics class 11
ABc of physics
Pattern. To Provide clarity of the subject, the whole text is studded with The Jargon, Key point, Watch out and Self-test Question Window to Formula forms a new feature of the present revised edition. It contains a direct and simple formula based Numerical Problem, which will tell the students as to how the formula derived in an article is to be used to solve the problem. The article work in each chapter of unit is coupled with well graded and carefully selected Solved Numerical Problems. These Solved Numerical Problems have been categorized into two Parts. I from Board Examinations and II from Competitive Engineering Examinations, such as I.I.T., Roorkee and I.S.M., Dhanbad. Many such problems have been provided with solutions by adopting a novel technique in the form of Thought Process.
Play with Graphs - Skills in Mathematics for JEE Main and Advanced
Amit M. Agarwal - 2015
Mathematical Methods in the Physical Sciences
Mary L. Boas - 1967
Intuition and computational abilities are stressed. Original material on DE and multiple integrals has been expanded.
It's a Numberful World: How Math Is Hiding Everywhere
Eddie Woo - 2019
. . like a pendulum? These may not look like math questions, but they are-because they all have to do with patterns. And mathematics, at heart, is the study of patterns. That realization changed Eddie Woo's life-by turning the "dry" subject he dreaded in high school into a boundless quest for discovery. Now an award-winning math teacher, Woo sees patterns everywhere: in the "branches" of blood vessels and lightning, in the growth of a savings account and a sunflower, even in his morning cup of tea! Here are twenty-six bite-size chapters on the hidden mathematical marvels that encrypt our email, enchant our senses, and even keep us alive-from the sine waves we hear as "music" to the mysterious golden ratio. This book will change your mind about what math can be. We are all born mathematicians-and It's a Numberful World.
Emerging Real Estate Markets: How to Find and Profit from Up-And-Coming Areas
David Lindahl - 2007
Buy all the copies on the bookshelf before your competitor does!" --Frank McKinney, "The Maverick Daredevil Real Estate Entrepreneur" and author of Frank McKinney's Maverick Approach to Real Estate Success "I've never seen another real estate book even come close to laying out a profit road map the way this one does. If your local economy is too hot or too cold, Lindahl's guide will show you how to invest in the up-and-coming markets with the greatest profit potential." --Stacy Kellams, President, www.RealEstateCourseReviews.com "Lindahl shows you how to look into the future and see where the next hot real estate markets will be. It's the closest thing I've found to a real estate crystal ball." --Jeff Adams, President, www.FreeRealEstateMentoring.com "The brilliant thing about this book is Lindahl's approach to investing by 'remote control.' He has a real-world system for living in one place and making money from investments in another." --William Bronchick, attorney and coauthor of Flipping Properties "In the crowded field of real estate gurus, Lindahl stands head-and-shoulders above the rest. This book is must reading for any serious investor--beginner or veteran." --Justin Ford, author of Seeds of Wealth and Main Street Millionaire