Sabtu, 06 Mei 2017

PDF Download Vino Italiano: Regional Wines of Italy

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  • Published on: 1600
  • Binding: Paperback

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Selasa, 02 Mei 2017

Download La Biblia Latinoamericana (Letra Grande, Rustica / Tapa Blanda)From San Pablo / Editorial Verbo Divino

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La Biblia Latinoamericana es la biblia Católica más vendida en el idioma español, con más de 70 millones vendidas al redor del mundo. La Biblia Latinoamericana, edición pastoral, es especialmente indicada para Latinoamérica, con presentación y comentarios de Bernardo Hurault. Contiene índices, pasajes paralelos y comentarios. La Biblia Latinoamericana es traducida de textos íntegros del hebreo y del griego, presentada y comentada para las comunidades cristianas de Latinoamérica y para los que buscan a Dios. Revisada en el 2005 e Impresa en España por la Sociedad Bíblica Católica Internacional (SOBICAN), editoriales San Pablo y Editorial Verbo Divino, esta edición es de LETRA GRANDE y TAPA RUSTICA / BLANDA. NOTA: Esta Biblia Tapa blanda es la unica permitida en las prisiones

  • Sales Rank: #475510 in Books
  • Published on: 2005
  • Binding: Paperback
  • 650 pages

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Senin, 01 Mei 2017

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  • Published on: 2017-01-03
  • Original language: English
  • Number of items: 1
  • Dimensions: .0" h x .0" w x .0" l, .0 pounds
  • Binding: Paperback
  • 224 pages

About the Author

Rossella Vanon is an award winning fashion and beauty photographer who has had her work exhibited in Times Square. She is currently based in London and has shot for clients including Nylon Mexico, Fashion 156, Cielo, Push it, Mediaset Italy, TAW London and appeared in many international photography learning communities including phlearn.com and PROFOTO. In between her photography schedule she finds time to teach workshops in London.

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Jumat, 28 April 2017

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In an efficient market, all stocks should be valued at a price that is consistent with available information. But as financial expert Vijay Singal, Ph.D., CFA, points out, there are circumstances under which certain stocks sell at a price higher or lower than the right price. In Beyond the Random Walk, Singal discusses ten such anomalous prices and shows how investors might--or might not--be able to exploit these situations for profit.

The author distills several decades of academic research into a focused discussion of market anomalies that is both accessible and useful to people with varied backgrounds. Past empirical evidence is supplemented with author's own research using more recent data. Anomalies covered include the "December Effect," "Momentum in Industry Stocks," "S&P 500 Index Changes," "Trading by Insiders," and "Merger Arbitrage." In each chapter, the author describes the particular anomaly, explains how it occurs, shows ways to take advantage of the anomaly, and highlights the risks involved. We learn, for example, that shares of stocks that have appreciated in recent months become scarce in late December, because investors wait until January before they sell (to postpone payment of taxes on profits). This scarcity drives the price up--the "December Effect"--and smart buyers can make the equivalent of 75% annual return on a five-day investment. Each chapter includes suggestions for further reading as well as tables and graphs that support the discussion. The book concludes with a preview of many other interesting anomalies and a section on how investor behavior might influence prices.

Clearly written and informative, this well-researched volume is a must read for investors, traders, market specialists, and students of financial markets.

  • Sales Rank: #1926123 in Books
  • Published on: 2006-06-01
  • Original language: English
  • Number of items: 1
  • Dimensions: 6.10" h x 1.10" w x 9.20" l, 1.21 pounds
  • Binding: Paperback
  • 384 pages

Review

"Beyond the Random Walk is the definitive work on how stocks can be persistently mis-priced, lucidly and comprehensively cataloging market inefficiencies. It offers investors sensible strategies to exploit valuable investment opportunities and is a critical reference for researchers." --Lawrence A. Cunningham, Boston College, author of The Essays of Warren Buffett: Lessons for Corporate America


"Vijay Singal's interesting book provides an overall treatment of the return anomalies that have gripped the attention of academics and investors alike. It is a good source for those who want to understand stock market anomalies, and a useful guide for those who want to trade on them. As always, those seeking to beat the market would do well to remind themselves about the perils of overconfidence, and the attendant risks that trading on sentiment entails."--Hersh Shefrin, Santa Clara University, author of Beyond Greed and Fear: Understanding Behavioral Finance and the Psychology of Investing


"We can thank Vijay Singal for categorizing the major stock market inefficiencies, and for telling us how to make money from them."--Roger G. Ibbotson, Yale School of Management, and Chairman, Ibbotson Associates


"Singal provides an easy-to-read introduction to the pricing anomalies discovered by academics and exploited by traders. Understanding these anomalies will help you identify the systematic mistakes traders make and how you might profit from them."--Larry Harris, Fred V. Keenan Chair in Finance, Marshall School of Business at USC, author of Trading & Exchanges: Market Microstructure for Practitioners


"Understanding how the markets work and how to exploit them for profit is what makes Wall Street tick. Successful investing is based on extracting small low risk profits; this is what separates good managers from great ones. By explaining how to look for, spot and take advantage of anomalies Singal is about to create a whole new class of great managers. It is clear that his research, experience, and expertise are something all investors should take advantage of as they try to navigate the randomness and not so randomness that is the market."--Daniel A. Strachman, author of Essential Stock Picking Strategies and Editor of The Strachman Report


"This is a comprehensive look at financial market anomalies in language that is accessible to all. While no book on the market offers foolproof ways of making money, this book will certainly be an instructive read and bring the lay person up to speed on the latest research."--Raghuram Rajan, Joseph Gidwitz Professor of Finance, University of Chicago


"This is a very nice summary of the academic literature on anomalies, and contains some intriguing suggestions for taking advantage of them." --Andrew W. Lo, MIT


"A great Christmas gift for investors."--SmartMoney


About the Author
Vijay Singal is J. Gray Ferguson Professor of Finance and Chairperson of the Finance Department at the Pamplin College of Business of Virginia Tech. Dr. Singal is an author of many academic and practitioner articles.

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Kamis, 27 April 2017

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  • Sales Rank: #71200 in Books
  • Published on: 2014-06-30
  • Format: Large Print
  • Original language: English
  • Number of items: 1
  • Dimensions: 7.81" h x .31" w x 5.06" l, .31 pounds
  • Binding: Paperback
  • 134 pages

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Senin, 17 April 2017

Ebook Download Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists


Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists


Ebook Download Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

About the Author

Alice is a technical leader in the field of Machine Learning. Her experience spans algorithm and platform development and applications. Currently, she is a Senior Manager in Amazon's Ad Platform. Previous roles include Director of Data Science at GraphLab/Dato/Turi, machine learning researcher at Microsoft Research, Redmond, and postdoctoral fellow at Carnegie Mellon University. She received a Ph.D. in Electrical Engineering and Computer science, and B.A. degrees in Computer Science in Mathematics, all from U.C. Berkeley.

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Product details

Paperback: 218 pages

Publisher: O'Reilly Media; 1 edition (April 14, 2018)

Language: English

ISBN-10: 1491953241

ISBN-13: 978-1491953242

Product Dimensions:

7 x 0.4 x 9.1 inches

Shipping Weight: 12.5 ounces (View shipping rates and policies)

Average Customer Review:

4.1 out of 5 stars

8 customer reviews

Amazon Best Sellers Rank:

#114,128 in Books (See Top 100 in Books)

It's great that someone's written a book about Feature Engineering and it contains a lot of interesting material. I also like that clear readable coding examples are included (Python), so that the reader can try at home.But, it feels unfinished. The book is pretty short and the set of topics seems more like what the authors were interested in than a survey of the field. The level of explanation varies from super-detailed to glossing over concepts and technical terms that most readers won't know.The biggest issue is that the graphics are mostly hand-drawn on some kind of tablet and look like they were scrawled out quickly. I've attached one example. As you can see, they didn't even bother to adjust the contrast. So, the images have a dull gray background. Disappointing that O'Reilly didn't take care of this for the authors.

A good reference book for those of us in the daily engagement of machine learning.

Good intro to the feature engineering with clear examples in Python. Overall 5 stars as the book is easy to read and it contains useful hints.

Well written. Highly recommended.

This book does a great job explaining the "why".

At the end of the preview, the book recommends converting song listening counts into a binary 0 1 variable. This is TERRIBLE advice. It mentions binning as an alternative but goes ahead with binary code. The authors argument is that "listening 10 times does not mean liking the song as much as someone who listens ,,20 times."In other words, if the user listened to a song at least once, then we count it as the user liking the song. This way, the model will not need to spend cycles on predicting the minute differences between the raw counts. The binary target is a simple and robust measure of user preference.". There are many ways to deal with possible non-proportional effects. Going to a 1 treats someone who tried a song and did not like it the same as someone who listens daily!I would love a good book on this topic for my students, but this is not it..

I have read almost half of the book. It is amazingly useful, concrete and helpful. I can't stop finishing the book and then write my review and I would like to appreciate authors for their great work.Based on my experience the process of machine learning is 80% data wrangling, cleaning, feature engineering,... and 20% running the model or ML algorithm. This book aims for the first 80%.

I’ve read the pre-release version on safari’s books. Excellently written and great examples. I thoroughly enjoyed the initial chapters on transforming input data.

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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists PDF

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists PDF

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists PDF
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists PDF