Solve For X

Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, the book uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.--Publisher's note.

This book constitutes the refereed proceedings of the 10th International Conference on Parallel Problem Solving from Nature, PPSN 2008, held in Dortmund, Germany, in September 2008. The 114 revised full papers presented were carefully reviewed and selected from 206 submissions. The conference covers a wide range of topics, such as evolutionary computation, quantum computation, molecular computation, neural computation, artificial life, swarm intelligence, artificial ant systems, artificial immune systems, self-organizing systems, emergent behaviors, and applications to real-world problems. The paper are organized in topical sections on formal theory, new techniques, experimental analysis, multiobjective optimization, hybrid methods, and applications.

• according to syllabus for exam up to year 2020 • new questions from top schools & colleges since 2008 – 2017 • exposes “surprise & trick” questions • complete answer keys • most efficient method of learning, hence saves time • arrange from easy-to-hard both by topics and question-types to facilitate easy absorption • full set of step-by-step solution approaches (available separately) • advanced trade book • complete and concise eBook editions available • also suitable for • Cambridge GCE AL (H1/H2) • Cambridge International A & AS Level • Books available for other subjects including Physics, Chemistry, Biology, Mathematics, Economics, English • Primary level, Secondary level, GCE O-level, GCE A-level, iGCSE, Cambridge A-level, Hong Kong DSE • visit www.yellowreef.com for sample chapters and more

In this “powerful personal story woven with a rich analysis of what we all seek” (Sergey Brin, cofounder of Google), Mo Gawdat, Chief Business Officer at Google’s [X], applies his superior logic and problem solving skills to understand how the brain processes joy and sadness—and then he solves for happy. In 2001 Mo Gawdat realized that despite his incredible success, he was desperately unhappy. A lifelong learner, he attacked the problem as an engineer would: examining all the provable facts and scrupulously applying logic. Eventually, his countless hours of research and science proved successful, and he discovered the equation for permanent happiness. Thirteen years later, Mo’s algorithm would be put to the ultimate test. After the sudden death of his son, Ali, Mo and his family turned to his equation—and it saved them from despair. In dealing with the horrible loss, Mo found his mission: he would pull off the type of “moonshot” goal that he and his colleagues were always aiming for—he would share his equation with the world and help as many people as possible become happier. In Solve for Happy Mo questions some of the most fundamental aspects of our existence, shares the underlying reasons for suffering, and plots out a step-by-step process for achieving lifelong happiness and enduring contentment. He shows us how to view life through a clear lens, teaching us how to dispel the illusions that cloud our thinking; overcome the brain’s blind spots; and embrace five ultimate truths. No matter what obstacles we face, what burdens we bear, what trials we’ve experienced, we can all be content with our present situation and optimistic about the future.

The world's greatest mental mathematical magician takes us on a spellbinding journey through the wonders of numbers (and more) "Arthur Benjamin . . . joyfully shows you how to make nature's numbers dance." -- Bill Nye (the science guy) The Magic of Math is the math book you wish you had in school. Using a delightful assortment of examples-from ice-cream scoops and poker hands to measuring mountains and making magic squares-this book revels in key mathematical fields including arithmetic, algebra, geometry, and calculus, plus Fibonacci numbers, infinity, and, of course, mathematical magic tricks. Known throughout the world as the "mathemagician," Arthur Benjamin mixes mathematics and magic to make the subject fun, attractive, and easy to understand for math fan and math-phobic alike. "A positively joyful exploration of mathematics." -- Publishers Weekly, starred review "Each [trick] is more dazzling than the last." -- Physics World

'Mansoulié here demonstrates his talent for communicating physics to non-scientists, his target audience for this brief, readable, volume.'CHOICEThe book comprises 15 short chapters, each presenting an important equation of Physics, from the simplest and oldest, to more complex and recent ones. The target audience is the interested general public, hence no mathematics is involved (beyond the simple expression of each equation).What can a professional 'read' in an equation? Does one see a rainbow differently when one knows the law of refraction of light? Do some equations tell more than what they were invented for? The book presents an opportunity to think about the nature of the physical laws (without writing a philosophy treatise): are they written in advance, or only the result of our imagination?Memories and personal quotes in the book underline the intimate relation between a scientist and his research, and the interplay with his personal life. Each chapter is illustrated by a full page artistic drawing by Lison Bernet, sometimes kind, sometimes funny, and always poetic.

Computational Learning Theory presents the theoretical issues in machine learning and computational models of learning. This book covers a wide range of problems in concept learning, inductive inference, and pattern recognition. Organized into three parts encompassing 32 chapters, this book begins with an overview of the inductive principle based on weak convergence of probability measures. This text then examines the framework for constructing learning algorithms. Other chapters consider the formal theory of learning, which is learning in the sense of improving computational efficiency as opposed to concept learning. This book discusses as well the informed parsimonious (IP) inference that generalizes the compatibility and weighted parsimony techniques, which are most commonly applied in biology. The final chapter deals with the construction of prediction algorithms in a situation in which a learner faces a sequence of trials, with a prediction to be given in each and the goal of the learner is to make some mistakes. This book is a valuable resource for students and teachers.