Includes many examples and figures. GENERAL TOPOLOGY. Set Theory and Logic. Topological Spaces and Continuous Functions. Connectedness and Compactness. Countability and Separation Axioms. The Tychonoff Theorem. Metrization Theorems and paracompactness. Complete Metric Spaces and Function Spaces. Baire Spaces and Dimension Theory. ALGEBRAIC TOPOLOGY. The Fundamental Group. Separation Theorems. The Seifert-van Kampen Theorem. Classification of Surfaces. Classification of Covering Spaces. Applications to Group Theory. For anyone needing a basic, thorough, introduction to general and algebraic topology and its applications.

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    Covers textual and linguistic matters; mathematical analyses of Euclid's ideas; commentators; refutations, supports, extrapolations, reinterpretations and historical notes. Vol. 1 includes Introduction, Books 1-2: Triangles, rectangles.

    Purchase includes a free trial membership in the publisher's book club where you can select from more than a million books without charge. Chapters: The Bean Trees, Pigs in Heaven, the Poisonwood Bible, Animal Dreams, Prodigal Summer. Source: Wikipedia. Free updates online. Not illustrated. Excerpt: The Bean Trees, first published in 1988, is the first book written by Barbara Kingsolver, followed by a sequel Pigs in Heaven. The protagonist of the novel, Taylor Greer, a native of Kentucky, finds herself in Oklahoma near Cherokee territory. The novel begins with a woman leaving a Cherokee infant with Taylor. The remainder of the novel traces the experiences of Taylor and the child, whom Taylor has named Turtle. Covering Turtle's early childhood, the story includes a colorful cast of characters, including a Guatemalan couple and Mattie, the owner of Jesus Is Lord Used Tires. The novel also makes reference to the issue of Native American parental rights. The Bean Trees opens in rural Kentucky. Taylor goes on to tell the story of how she is scared of tires. Taylor was the one to escape small-town life. She did so by avoiding pregnancy, getting a job working at the hospital, and saving up enough money to buy herself an old Volkswagen bug. About five years after high school graduation, she decides to go on a journey to see what life has to offer her. Her car breaks down in the middle of the Cherokee Nation in Oklahoma. As she sits in her car, getting ready to leave, a woman approaches and puts a baby in the front seat of Taylors car, telling her to take it. She tells Taylor she is the sister of the childs mother and that the baby was born in a Plymouth car. The woman leaves with no further explanation. Taylor is bewildered, but drives off with the child. They go to a hotel, and while bathing the baby, Taylor discovers that the baby, a girl, has been abused and sexually molested. She names the baby Tur...More: http: //booksllc.net/?id=138820

    

    No previous knowledge of statistics is assumed, and mathematical background is assumed to be minimal (lowest-level high-school algebra). This text may be used in a one or two course sequence. Such sequences are commonly required of social science graduate students in sociology, political science, and psychology. Students in geography, anthropology, journalism, and speech also are sometimes required to take at least one statistics course.

    It is universally recognized as the standard English version of Aristotle. This revised edition contains the substance of the original translation, slightly emended in light of recent scholarship; three of the original versions have been replaced by new translations; and a new and enlarged selection of Fragments has been added. The aim of the translation remains the same: to make the surviving works of Aristotle readily accessible to English speaking readers.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    This introductory text is suitable for use in a course on the subject or for self-study, featuring broad coverage and a readable exposition, with many examples and exercises. The four main chapters present the basics: fundamental group and covering spaces, homology and cohomology, higher homotopy groups, and homotopy theory generally. The author emphasizes the geometric aspects of the subject, which helps students gain intuition. A unique feature is the inclusion of many optional topics not usually part of a first course due to time constraints: Bockstein and transfer homomorphisms, direct and inverse limits, H-spaces and Hopf algebras, the Brown representability theorem, the James reduced product, the Dold-Thom theorem, and Steenrod squares and powers.

    

    The approach taken here uses elementary versions of modern methods found in sophisticated mathematics. The formal prerequisites include only a term of linear algebra, a nodding acquaintance with the notation of set theory, and a respectable first-year calculus course (one which at least mentions the least upper bound (sup) and greatest lower bound (inf) of a set of real numbers). Beyond this a certain (perhaps latent) rapport with abstract mathematics will be found almost essential.

    The main ideas and applications of the metallurgy are provided in this book.

    And, should you ever get to hold one, you will find the human heart to be rubbery and shockingly light.'What Could Possibly Go Wrong? is a report from the front line of emergency medicine, the first ever account of what it is like to work as an air ambulance doctor. Whether describing cutting through a patient's breastbone to plug a stab wound or barrel rolling a light aircraft at 5,000 feet, Tony Bleetman captures the sheer adrenaline of racing through the sky to save lives. You will learn how to land a helicopter on the side of a mountain, what it means to encounter death every day, and how to perform a tracheotomy in real life (clue: it doesn’t involve a ball-point pen).Funny, shocking and moving, What Could Possibly Go Wrong? is a glimpse at a world where the wrong decision can mean the difference between life and death.Originally published as You Can't Park There: The Highs and Lows of an Air Ambulance Doctor.

    The contributors are all distinguished experts in their respective fields.All the contributors are scientists working in Indian laboratories, however their achievements in the field are full of valuable information supplemented with adequate references which help the intended readers in digging out the complete information on any aspect.The book has 17 chapters, 150 figures and over 2150 references and will be of immense use for all pharmaceutical industries, RD laboratories, research scientists in universities colleges, teachers as well as post-graduate and graduate students.