It is the thing that snarls at us when we think about making a change in our lives and stops us developing and achieving our potential. In Taming Tigers Jim Lawless shares his proven and inspirational training programme to help you achieve your dreams by taming the Tigers in your life.Now for the first time, you can learn how to use these highly practical rules to overcome your fears and do things you never thought you could - in both your professional and private life.1.Act boldly today - time is limited2.Re-write your rulebook - challenge it hourly3.Head in the direction of where you want to arrive, every day4.It's all in the mind5.The tools for Taming Tigers are all around you6.There is no safety in numbers7.Do something scary everyday8.Understand and control your time to create change9.Create disciplines - do the basics brilliantly10.Never, never give up!Read case studies from people who have changed their lives by following the rules, and hear about Jim's experience of grabbing his own Tiger by the tail, as he went from a thirty-six-year-old overweight non-riding consultant, to a fully-fledged jockey and UK freediving record holder in 12 months - proof that Taming Tigers works!

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    In Bringing Out the Best in Yourself at Work, Enneagram teacher and business consultant Ginger Lapid-Bodga shows professionals how to apply this popular tool to their work as a way to improve their productivity and help them build positive relationships among coworkers. This practical guide explains how to use the Enneagram to:Communicate more effectivelyProvide constructive feedbackPrevent and resolve conflictBring out their strongest leadership skillsDiscover methods for professional developmentWork better in teams

    In spite of seeming completeness, he suffered from inner chaos and restlessness which resulted in constant panic attacks. And suddenly everything stopped and he was helpless. He recognizes his incurable illness and decides to end his life but fortunately he fails before the attempt. But, his destiny unfolds a rare experience and he sees things which he thought did not exist. He leaves everything that he possessed behind and sets off on a random journey. Jako travels and experiences things which were beyond his imagination. He meets people who deceive and delude him, though he travelled to find answers, Jako returns home with treachery and delusion. On his way back, Jako reaches a small town with an isolated beach. He stays there meets a mysterious traveler; at first, Jako was confused to consider this man as a guardian angel or a mentalist. But as time passes they get along and this mysterious man solves all the riddles that surround Jako. They both sit at the beach and discuss life, Destiny, Freewill, Dreams, Dejavu's, Reality, Hypocrisy, Philosophy, Pleasure, God, Beauty, Love, Infatuation, Psychology, Wisdom, Intellect, Happiness, Boredom. Jako throws strange questions at this man and he answers them with wise stories. Jako answers all his questions by himself. Jako finishes his remarkable journey and returns home rehabilitated. P.S. : Kindly write a few words/lines review about this book. It will inspire others to read it.

    Twenty years ago when Falk was accused of murder, Luke was his alibi. Falk and his father fled under a cloud of suspicion, saved from prosecution only because of Luke’s steadfast claim that the boys had been together at the time of the crime. But now more than one person knows they didn’t tell the truth back then, and Luke is dead.Amid the worst drought in a century, Falk and the local detective question what really happened to Luke. As Falk reluctantly investigates to see if there’s more to Luke’s death than there seems to be, long-buried mysteries resurface, as do the lies that have haunted them. And Falk will find that small towns have always hidden big secrets.

    Where we don't, we read, we ask, we learn and then, we solve! What happens when there are no answers though? When nobody in the world knows! When we see the need to invent Gods even if we can't discover Him. Through a string of poems, the author narrates such an experience with his non-verbal and autistic daughter, Enya. What started as a week of babysitting for him soon became a seeking to change her into 'normal'. But, that seeking ended up transforming the seeker!The narrative in the form of poetry touches upon the revelation that comes out of desperation of not finding an answer at all and therefore, the thoughts getting tired of themselves and the mind taking a back seat. In that silence, the author says, things become clear and all aspects of life show their inter-relation! The intellect gives way to the intelligence, the body and mind as 'me' gives way to the world as 'me'! The mind map once seen, one starts to see the true nature of the 'me' and that perspective and clarity make everything clear and possible in life...

    It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of "data science" for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (or MOOC) offered by the pioneering MIT--Harvard collaboration edX.Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. The book does not require knowledge of mathematics beyond high school algebra, but does assume that readers are comfortable with rigorous thinking and not intimidated by mathematical concepts. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming.Introduction to Computation and Programming Using Python can serve as a stepping-stone to more advanced computer science courses, or as a basic grounding in computational problem solving for students in other disciplines.

    Sport makes them think, engage and argue. Given that people already take sport so very seriously, and at such an intense level of enquiry, then Ed Smith concludes we should draw out some of sport's intellectual lessons and practical uses What Sport Teaches Us About Life gives us a rare glimpse into the world of sport as seen from an extraordinarily keen, and closely-involved observer. In one chapter Smith extols the virtues of amateurism in today's professional world; in another he explains why there'll never be another sportsman as dominant as Don Bradman. He unearths the hidden dimensions of England's 2005 Ashes win, examines the impact of the free market on cricket and football, argues that cheating is not always as clear cut as it might seem.

    Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

    From the movement of flocks of birds to the Internet, environmental sustainability, and market regulation, the study and understanding of complex non-linear systems has become highly influential over the last 30 years.In this Very Short Introduction, one of the leading figures in the field, John Holland, introduces the key elements and conceptual framework of complexity. From complex physical systems such as fluid flow and the difficulties of predicting weather, to complex adaptive systems such as the highly diverse and interdependent ecosystems of rainforests, he combines simple, well-known examples -- Adam Smith's pin factory, Darwin's comet orchid, and Simon's 'watchmaker' -- with an account of the approaches, involving agents and urn models, taken by complexity theory.ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.

    Essential reading for T20 fans and traditional followers’ - Scyld Berry‘An invaluable guide by two smart young writers to the story of T20 cricket, to the strategies that underpin it and to the players who have made the format’ - Mike Atherton‘Tim and Freddie are the torchbearers of tomorrow, we must listen to them’ - Harsha Bhogle‘T20 is here to stay and we should celebrate and cherish all the skills involved . . . it is only going to get bigger’ - Michael VaughanCricket 2.0 tells the story of how an old, traditional game was transformed by Twenty20 and how this format moved from being a gimmick to the face of modern cricket.Using exclusive interviews with those at its heart, Tim Wigmore and Freddie Wilde chronicle this revolution with insight, forensic analysis and story-telling verve.The iconic captain Brendon McCullum, England’s T20 visionaries Eoin Morgan and Jos Buttler and Trinidad’s Kieron Pollard and Sunil Narine, who rose to become among the first T20 millionaires, explain how they shaped T20 - and how it shaped them. Test greats Rahul Dravid and Ricky Ponting recount what a sea-change T20 represented and decode T20 strategy. AB de Villiers explores the limits of modern batting. The Afghan phenomenon Rashid Khan shows that T20 superstars can now come from anywhere. Venky Mysore, the cricket revolutionary you have never heard of, reveals how the game is changing off the field.Through a prism of compelling human-interest stories and featuring interviews with more than fifty players and coaches, Cricket 2.0 examines how a cocktail of globalisation, new aggressive tactics and huge investment are changing the sport faster than ever before, while analysing the myriad ways in which a traditional game has been revolutionised forever, both on and off the pitch.Told with vivid clarity and insight, this is the extraordinary and previously misunderstood story of Twenty20 cricket and how it is shaping the future of the game.

    "Web Analytics 2.0" presents a new framework that will permanently change how you think about analytics. It provides specific recommendations for creating an actionable strategy, applying analytical techniques correctly, solving challenges such as measuring social media and multichannel campaigns, achieving optimal success by leveraging experimentation, and employing tactics for truly listening to your customers. The book will help your organization become more data driven while you become a super analysis ninja Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.

    Algorithms have made our lives more efficient, more entertaining, and, sometimes, better informed. At the same time, complex algorithms are increasingly violating the basic rights of individual citizens. Allegedly anonymized datasets routinely leak our most sensitive personal information; statistical models for everything from mortgages to college admissions reflect racial and gender bias. Meanwhile, users manipulate algorithms to "game" search engines, spam filters, online reviewing services, and navigation apps.Understanding and improving the science behind the algorithms that run our lives is rapidly becoming one of the most pressing issues of this century. Traditional fixes, such as laws, regulations and watchdog groups, have proven woefully inadequate. Reporting from the cutting edge of scientific research, The Ethical Algorithm offers a new approach: a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. Michael Kearns and Aaron Roth explain how we can better embed human principles into machine code - without halting the advance of data-driven scientific exploration. Weaving together innovative research with stories of citizens, scientists, and activists on the front lines, The Ethical Algorithm offers a compelling vision for a future, one in which we can better protect humans from the unintended impacts of algorithms while continuing to inspire wondrous advances in technology.

    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.