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Each year 10,000 prospective dental students take the DAT. Filled with powerful strategies for scoring higher on the DAT, content review, and practice tests with detailed answer explanations, Kaplan's acclaimed DAT review package is an indispensable tool. Charts & diagrams.
2019 Update - We have made revisions to correct for minor errata. For a complete list of updates made, please visit us at www.thetutorverse.com. --- The Upper Level ISEE can be a very challenging test. Extra practice can make all the difference between a good score and a great score. That's why this book has more questions than even 10 full-length exams - well over 1,500 practice questions dedicated only to the Upper Level ISEE. You won't find any material in this book related to another test - there's no filler here! In this book you will find: * Two full-length tests; a diagnostic test to help you pinpoint the areas in most need of improvement, and a practice test to help familiarize students with the real thing. * Critical skills and concepts broken out by topic, so students can zero-in on key areas. * Questions that progress in difficulty, to help students expand their knowledge base and prepare for tough questions. * 100 pages of detailed answer explanations available online at www.thetutorverse.com. This book can be used for independent practice or for study with a professional educator. For best results, we recommend using this book with a tutor or teacher who can help students learn more about new or particularly challenging topics.
A practical approach to using regression and computation to solve real-world problems of estimation, prediction, and causal inference.
How our collective intelligence has helped us to evolve and prosper Humans are a puzzling species. On the one hand, we struggle to survive on our own in the wild, often failing to overcome even basic challenges, like obtaining food, building shelters, or avoiding predators. On the other hand, human groups have produced ingenious technologies, sophisticated languages, and complex institutions that have permitted us to successfully expand into a vast range of diverse environments. What has enabled us to dominate the globe, more than any other species, while remaining virtually helpless as lone individuals? This book shows that the secret of our success lies not in our innate intelligence, but in our collective brains—on the ability of human groups to socially interconnect and learn from one another over generations. Drawing insights from lost European explorers, clever chimpanzees, mobile hunter-gatherers, neuroscientific findings, ancient bones, and the human genome, Joseph Henrich demonstrates how our collective brains have propelled our species' genetic evolution and shaped our biology. Our early capacities for learning from others produced many cultural innovations, such as fire, cooking, water containers, plant knowledge, and projectile weapons, which in turn drove the expansion of our brains and altered our physiology, anatomy, and psychology in crucial ways. Later on, some collective brains generated and recombined powerful concepts, such as the lever, wheel, screw, and writing, while also creating the institutions that continue to alter our motivations and perceptions. Henrich shows how our genetics and biology are inextricably interwoven with cultural evolution, and how culture-gene interactions launched our species on an extraordinary evolutionary trajectory. Tracking clues from our ancient past to the present, The Secret of Our Success explores how the evolution of both our cultural and social natures produce a collective intelligence that explains both our species' immense success and the origins of human uniqueness.
This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.