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"A true benchmark. This work will set a new standard for the conceptualization--let alone the study--of missionization and religious conversion, colonial language policy, and language-oriented social history. Hanks provides a framework for thinking about language history that integrates language ideology, linguistic form (from phonology to speech genres), social organization, and the phenomenology of experience that goes so far beyond traditional historical, linguistic, or philological perspectives as to constitute a new paradigm for the field. Converting Words will be a classic work that will stimulate others to emulate Hanks's powerful scholarly example. The field will never be the same after this book appears."--Richard Bauman, author of A World of Others' Words: Cross-Cultural Perspectives on Intertextuality "Hanks's work is utterly original and unprecedented... I don't think historians of the Mesoamerican colonial regimes should write anything until they read this book; it's that important."--Jane H. Hill, author of A Grammar of Cupeño
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This volume considers the concept of conversion as a tool for understanding transformations to modernity. It examines conversions to modernity within the Ottoman domain, India, China, and Japan as a reaction to the pressures of colonialism and imperialism.
27000 English Words Dictionary With Definitions is a great resource anywhere you go; it is an easy tool that has just the words completed description you want and need! The entire dictionary is an alphabetical list of English words with their full description plus special Alphabet, Irregular Verbs and Parts of speech. It will be perfect and very useful for everyone who needs a handy, reliable resource for home, school, office, organization, students, college, government officials, diplomats, academics, professionals, business people, company, travel, interpreting, reference and learning English. The meaning of words you will learn will help you in any situations in the palm of your hand.
Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. What You Will Learn Understand the Statistical and Machine Learning fundamentals necessary to build models Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages Analyze the results and tune the model appropriately to your own predictive goals Understand the concepts of required statistics for Machine Learning Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models Learn reinforcement learning and its application in the field of artificial intelligence domain In Detail Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more. By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Style and approach This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.