Falling apart and bowing down to the miseries of a broken family and a daunting past, at 24, they decided to move on. But life pulled out one trick after another to bring them back to each other and give one last chance to do what they couldn't do in the past twelve years. A choice had to be made and there was no going back. Could they learn from the echoing footsteps of their parents and speak their heart out or the fear of saying the unsaid killed it once again for them? Did their time to fall in love come, or like always, they met, greeted and departed to part forever?

    Three Guides in One! Identification, Values, Lapidary UsesDesigned with beginners in mind, yet filled with valuable technical information for advanced collectors, "Collecting Rocks, Gems and Minerals" takes you from being just someone who appreciates rocks to a true "collector."Easy-to-use, quick reference format arranged by category and color of stoneCovers both lapidary and mineral display materialsProvides values and tips for locating, buying and collectingIncludes organics such as amber, bone, coral, pearl and shellLists chemical group, system, hardness, opacity, fracture, specific gravity and moreContains more than 650 full-color photosForeword by Johann Zenz, world renowned agate expert, author and lecturer

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    He is also the Head Teaching Fellow for the most popular course at Harvard, “Positive Psychology,” which is taken by more than 1,000 students per semester and led by Professor Tal Ben-Shahar.  Shawn received his B.A. in English from Harvard and a Master’s from Harvard Divinity School in Christian and Buddhist Ethics. Part of his interest in positive psychology stems from a troubling fact: studies have shown that many of Harvard’s undergraduates suffer from depression at some point in their college careers. One Day University is a unique educational experience  that brings intellectuals together to learn from top rated professors at Yale, Harvard, Stanford, Columbia and other prestigious universities. Chosen for their excellent teaching abilities as rated by their students, these great thinkers represent a wide variety of academic disciplines and share their knowledge in 60 minute, highly entertaining lectures. Offering the ability to learn the highlights of academic thought in world affairs, politics, history, science, art, and more; One Day University is a way to truly enjoy the thrill of learning without the pressures of tests and the high price tag of college tuition. Once reserved only for students who could attend the lectures in New York and other major cities, One Day University courses are now available to everyone from the comfort of their own homes in Kindle format.

    This is the first book to look at fracking from both economic and environmental perspectives."

    Switching from thin tailed to fat tailed distributions requires more than "changing the color of the dress." Traditional asymptotics deal mainly with either n=1 or n=∞, and the real world is in between, under the "laws of the medium numbers"-which vary widely across specific distributions. Both the law of large numbers and the generalized central limit mechanisms operate in highly idiosyncratic ways outside the standard Gaussian or Levy-Stable basins of convergence. A few examples: - The sample mean is rarely in line with the population mean, with effect on "na�ve empiricism," but can be sometimes be estimated via parametric methods. - The "empirical distribution" is rarely empirical. - Parameter uncertainty has compounding effects on statistical metrics. - Dimension reduction (principal components) fails. - Inequality estimators (Gini or quantile contributions) are not additive and produce wrong results. - Many "biases" found in psychology become entirely rational under more sophisticated probability distributions. - Most of the failures of financial economics, econometrics, and behavioral economics can be attributed to using the wrong distributions. This book, the first volume of the Technical Incerto, weaves a narrative around published journal articles.

    The set includes thirty-one 30-minute MCAT practice exams with answers and explanations and more than 1,600 MCAT questions.

    Along the way we’ll learn how selection bias can explain why your boss doesn’t know he sucks (even when everyone else does); how to use Bayes’ Theorem to decide if your partner is cheating on you; and why Mark Zuckerberg should never be used as an example for anything. See the world in a whole new light, and make better decisions and judgements without ever going near a t-test. Think. Think Statistically.

    Instead, ants decide what to do based on the rate, rhythm, and pattern of individual encounters and interactions--resulting in a dynamic network that coordinates the functions of the colony. Ant Encounters provides a revealing and accessible look into ant behavior from this complex systems perspective.Focusing on the moment-to-moment behavior of ant colonies, Deborah Gordon investigates the role of interaction networks in regulating colony behavior and relations among ant colonies. She shows how ant behavior within and between colonies arises from local interactions of individuals, and how interaction networks develop as a colony grows older and larger. The more rapidly ants react to their encounters, the more sensitively the entire colony responds to changing conditions. Gordon explores whether such reactive networks help a colony to survive and reproduce, how natural selection shapes colony networks, and how these structures compare to other analogous complex systems. Ant Encounters sheds light on the organizational behavior, ecology, and evolution of these diverse and ubiquitous social insects.

    Like its predecessors, the seventh edition includes the absolute minimum of mathematical/statistical notation necessary to teach the material. Concepts are fully explained in simple, easy-to-understand language as they are presented, making the book an excellent source from which to learn and teach. After each discussion, readers are guided through real-world examples to show how book principles work in professional practice. Includes easy-to-understand explanations of difficult statistical topics, such as sampling distributions, relationship between confidence level and confidence interval, interpreting r-square. A complete package of teaching/learning aids is provided in every chapter, including chapter review exercises, chapter concepts tests,"Statistics at Work" conceptual cases, "Computer Database Exercises," "From the Textbook to the Real-World Examples." This ISBN is in two volumes Part A and Part B.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    This book charts its growth and development by sampling from the work of some of its foremost practitioners, beginning with Isaac Newton and Gottfried Wilhelm Leibniz in the late seventeenth century and continuing to Henri Lebesgue at the dawn of the twentieth--mathematicians whose achievements are comparable to those of Bach in music or Shakespeare in literature. William Dunham lucidly presents the definitions, theorems, and proofs. Students of literature read Shakespeare; students of music listen to Bach, he writes. But this tradition of studying the major works of the masters is, if not wholly absent, certainly uncommon in mathematics. This book seeks to redress that situation.Like a great museum, The Calculus Gallery is filled with masterpieces, among which are Bernoulli's early attack upon the harmonic series (1689), Euler's brilliant approximation of pi (1779), Cauchy's classic proof of the fundamental theorem of calculus (1823), Weierstrass's mind-boggling counterexample (1872), and Baire's original category theorem (1899). Collectively, these selections document the evolution of calculus from a powerful but logically chaotic subject into one whose foundations are thorough, rigorous, and unflinching--a story of genius triumphing over some of the toughest, most subtle problems imaginable.Anyone who has studied and enjoyed calculus will discover in these pages the sheer excitement each mathematician must have felt when pushing into the unknown. In touring The Calculus Gallery, we can see how it all came to be.

    The physical sciences and engineering claim to be the basis of modern technology and therefore of modern life; the social sciences discuss big issues of governance, for example, democracy and taxation; the humanities claim that they revive your soul after it has been deadened by exposure to the physical and social sciences and to engineering. Where does the subject "games of strategy," often also called game theory, fit into this picture, and why should you study it? Dixit and Skeath's Games of Strategy offers a practical motivation much more individual and closer to your personal concerns than most other subjects. You play games of strategy all the time: with your parents, siblings, friends, enemies, even with your professors. You have probably acquired a lot of instinctive expertise, and we hope you will recognize in what follows some of the lessons you have already learned. This book's authors will build on this experience, systematize it, and develop it to the point where you will be able to improve your strategic skills and use them more methodically. Opportunities for such uses will appear throughout the rest of your life; you will go on playing such games with your employers, employees, spouses, children, and even strangers. Not that the subject lacks wider importance. Similar games are played in business, politics, diplomacy, wars--in fact, whenever people interact to strike mutually agreeable deals or to resolve conflicts. Being able to recognize such games will enrich your understanding of the world around you, and will make you a better participant in all its affairs.

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    Rocketed to popularity by the 2003 bestseller Moneyball and the film of the same name, the use of sabermetrics to analyze player performance has appeared to be a David to the Goliath of systemically advantaged richer teams that could be toppled only by creative statistical analysis. The story has been so compelling that, over the past decade, team after team has integrated statistical analysis into its front office. But how accurately can crunching numbers quantify a player's ability? Do sabermetrics truly level the playing field for financially disadvantaged teams? How much of the baseball analytic trend is fad and how much fact?The Sabermetric Revolution sets the record straight on the role of analytics in baseball. Former Mets sabermetrician Benjamin Baumer and leading sports economist Andrew Zimbalist correct common misinterpretations and develop new methods to assess the effectiveness of sabermetrics on team performance. Tracing the growth of front office dependence on sabermetrics and the breadth of its use today, they explore how Major League Baseball and the field of sports analytics have changed since the 2002 season. Their conclusion is optimistic, but the authors also caution that sabermetric insights will be more difficult to come by in the future. The Sabermetric Revolution offers more than a fascinating case study of the use of statistics by general managers and front office executives: for fans and fantasy leagues, this book will provide an accessible primer on the real math behind moneyball as well as new insight into the changing business of baseball.