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Selasa, 30 Oktober 2018

Download PDF Classical Art: From Greece to Rome (Oxford History of Art)By Mary Beard, John Henderson

Download PDF Classical Art: From Greece to Rome (Oxford History of Art)By Mary Beard, John Henderson

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Classical Art: From Greece to Rome (Oxford History of Art)By Mary Beard, John Henderson

Classical Art: From Greece to Rome (Oxford History of Art)By Mary Beard, John Henderson


Classical Art: From Greece to Rome (Oxford History of Art)By Mary Beard, John Henderson


Download PDF Classical Art: From Greece to Rome (Oxford History of Art)By Mary Beard, John Henderson

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Classical Art: From Greece to Rome (Oxford History of Art)By Mary Beard, John Henderson

The stunning masterpieces of Ancient Greece and Rome are fundamental to the story of art in Western culture and to the origins of art history. The expanding Greek world of Alexander the Great had an enormous impact on the Mediterranean superpower of Rome. Generals, rulers, and artists seized, imitated, and re-thought the stunning legacy of Greek painting and sculpture, culminating in the greatest art-collector the world had ever seen: the Roman emperor Hadrian.

This exciting new look at Classical art starts with the excavation of the buried city of Pompeii, and investigates the grandiose monuments of ancient tyrants, and the sensual beauty of Apollo and Venus. Concluding with that most influential invention of all, the human portrait, it highlights the re-discovery of Classical art in the modern world, from the treasure hunts of Renaissance Rome to scientific retrieval of artworks in the twenty-first century.

  • Sales Rank: #215634 in Books
  • Brand: Beard, Mary/ Henderson, John
  • Published on: 2001-07-19
  • Original language: English
  • Number of items: 1
  • Dimensions: 6.30" h x .80" w x 9.10" l, 1.59 pounds
  • Binding: Paperback
  • 304 pages

Review

"This is no conventional book on Classical Art, but a critique of Greek art through Roman eyes, analyzing the complexity of the Romans' reception of Greek pictorial and sculptural 'masterpieces,' most of which are only known today through their Roman versions."--Richard Brilliant, Columbia University


"Innovative, challenging, and never dull, this is a bracing departure from the norm. Readers will welcome its strong thesis and trenchant refusal to take received wisdom on trust."--Andrew Stewart, University of California, Berkeley


About the Author

Mary Beard and John Henderson both teach Classics at the University of Cambridge. Mary Beard is a fellow of Newnham College, and John Henderson is a fellow of King's College, Cambridge.

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Rabu, 24 Oktober 2018

Free PDF Plante kan praat (Afrikaans Edition)By Jeanne Goosen

Free PDF Plante kan praat (Afrikaans Edition)By Jeanne Goosen

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Plante kan praat (Afrikaans Edition)By Jeanne Goosen

Plante kan praat (Afrikaans Edition)By Jeanne Goosen


Plante kan praat (Afrikaans Edition)By Jeanne Goosen


Free PDF Plante kan praat (Afrikaans Edition)By Jeanne Goosen

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Plante kan praat (Afrikaans Edition)By Jeanne Goosen

Oplaas weer ’n kortverhaalbundel (van sowat vyftien verhale) deur die veelsydige en bekroonde skrywer, Jeanne Goosen. Plante kan praat beskryf opnuut die gryse voorstedelikheid van Parow in verhale soos "Spokie Snygans maak sy buiging" en "Lemoenboom", maar tegelykertyd roep Goosen ook ’n ander wêreld op: waar dooie siele met mekaar kommunikeer, waar Lubbert Das tuinier word vir ’n hertogin, en plante - nes woorde en musiek - uiteindelik al balsem vir die droefheid en ontgogeling van ons bestaan is

  • Amazon Sales Rank: #2133873 in eBooks
  • Published on: 2010-06-01
  • Released on: 2011-06-21
  • Format: Kindle eBook

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Jumat, 12 Oktober 2018

Download Ebook A First Course in Machine Learning, Second EditionBy Simon Rogers, Mark Girolami

Download Ebook A First Course in Machine Learning, Second EditionBy Simon Rogers, Mark Girolami

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A First Course in Machine Learning, Second EditionBy Simon Rogers, Mark Girolami

A First Course in Machine Learning, Second EditionBy Simon Rogers, Mark Girolami


A First Course in Machine Learning, Second EditionBy Simon Rogers, Mark Girolami


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A First Course in Machine Learning, Second EditionBy Simon Rogers, Mark Girolami

"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC."
―Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden

"This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade."
―Daniel Barbara, George Mason University, Fairfax, Virginia, USA

"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts."
―Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark

"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength…Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."
―David Clifton, University of Oxford, UK

"The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book."
―Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK

"This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning…The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective."
―Guangzhi Qu, Oakland University, Rochester, Michigan, USA

  • Sales Rank: #856962 in Books
  • Published on: 2016-08-15
  • Original language: English
  • Number of items: 1
  • Dimensions: 9.25" h x 1.10" w x 6.14" l, .0 pounds
  • Binding: Paperback
  • 427 pages

Review

"This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning. The prerequisites on math or statistics are minimal and following the content is a fairly easy process. The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective."
―Guangzhi Qu, Oakland University, Rochester, Michigan, USA

"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts. One of the strengths of the book is its practical approach. An extensive collection of code written in MATLAB/Octave, R, and Python is available from an associated web page that allows the reader to change models and parameter values to make [it] easier to understand and apply these models in real applications. The authors [also] introduce more advanced, state-of-the-art machine learning methods, such as Gaussian process models and advanced mixture models, which are used across machine learning. This makes the book interesting not only to students with little or no background in machine learning but also to more advanced graduate students interested in statistical approaches to machine learning."
―Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark

"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC."
―Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden

"This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade."
―Daniel Barbara, George Mason University, Fairfax, Virginia, USA

"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength. While there are other books available that aim for completeness, with exhaustively comprehensive introductions to every branch of machine learning, the book by Rogers and Girolami starts with the basics, builds a solid and logical foundation of methodology, before introducing some more advanced topics. The essentials of the model construction, validation, and evaluation process are communicated clearly and in such a manner as to be accessible to the student taking such a course. I was also pleased to see that the authors have not shied away from producing algebraic derivations throughout, which are for many students an essential part of the learning process―many other texts omit such details, leaving them as ‘an exercise for the reader.’ Being shown the explicit steps required for such derivations is an important part of developing a sense of confidence in the student. Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."
―David Clifton, University of Oxford, UK

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A First Course in Machine Learning, Second EditionBy Simon Rogers, Mark Girolami PDF