World History

    This lively and lucid book provides an introduction to the essential phenomenological concepts that are crucial for understanding great thinkers such as Husserl, Heidegger, and Merleau-Ponty. Written by a leading expert in the field, Dan Zahavi examines and explains key questions such as:- What is a phenomenological analysis?- What are the methodological foundations of phenomenology?- What does phenomenology have to say about embodiment and intersubjectivity?- How is phenomenology distinguished from, and related to, other fields in philosophy?- How do ideas from classic phenomenology relate to ongoing debates in psychology and qualitative research?With a glossary of key terms and suggestions for further reading, the book considers key philosophical arguments around phenomenology, making this an ideal starting point for anyone seeking a concise and accessible introduction to the rich and complex study of phenomenology.

    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

    This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

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    Here, complete with detailed illustrations and basic as well as advanced solving techniques, is the ultimate speedcuber’s guide. It not only gives the solution to the familiar 3x3x3 cube (which has 43,252,003,274,489,856,000—that’s 43 quintillion—possible positions), but also the 2x2x2, 4x4x4, and staggeringly difficult 5x5x5 puzzles. With millions of cubes out there and countless would-be champions looking for tips to improve their times, this is the definitive manual for this unique sport.